Internet-Draft                                                      lake
Intended status: standards-track                              March 2026
Expires: September 05, 2026


         Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments
         draft-agent-ecosystem-agent-learning-protocol-00

Abstract

   Federated learning enables distributed AI agents to
   collaboratively improve models without centralizing training data.
   However, existing federated architectures lack comprehensive
   privacy guarantees for multi-tenant environments where agents from
   different organizations must learn together while preventing data
   leakage. This specification defines the Privacy-Preserving Agent
   Learning Protocol (PPALP) that combines differential privacy,
   secure aggregation, and zero-knowledge proofs to enable federated
   agent learning with formal privacy guarantees. PPALP provides
   mechanisms for privacy-preserving model updates, tenant isolation
   enforcement, and cryptographic verification of privacy compliance.
   The protocol supports heterogeneous agent types while maintaining
   backward compatibility with existing federated learning
   frameworks. This work addresses critical privacy gaps in federated
   agent architectures and provides implementable solutions for
   secure cross-organizational AI collaboration.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   This document is intended to have standards-track status.
   Distribution of this memo is unlimited.

Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
   NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
   "MAY", and "OPTIONAL" in this document are to be interpreted as
   described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
   appear in all capitals, as shown here.

   Privacy Budget
      A quantitative measure of the privacy cost incurred by
      releasing information about a dataset through differential
      privacy mechanisms

   Tenant Isolation
      The property that agent learning activities of one
      organizational tenant cannot infer private information about
      another tenant's data or models

   Secure Aggregation
      A cryptographic protocol that allows computation of aggregate
      statistics over distributed data without revealing individual
      contributions

   Gradient Inversion Attack
      An attack where an adversary attempts to reconstruct training
      data from shared gradient information in federated learning

   Privacy Compliance Verifier
      A system component that cryptographically verifies adherence to
      differential privacy guarantees and tenant isolation
      requirements

   Federated Coordinator
      The central entity responsible for orchestrating privacy-
      preserving model aggregation across multiple tenants in
      federated learning


Table of Contents

   1.  Introduction  ................................................  3
   2.  Terminology  .................................................  4
   3.  Problem Statement  ...........................................  5
   4.  Privacy-Preserving Agent Learning Architecture  ..............  6
   5.  Differential Privacy for Agent Model Updates  ................  7
   6.  Secure Multi-Tenant Federated Aggregation  ...................  8
   7.  Privacy Compliance and Verification  .........................  9
   8.  Protocol Message Formats and Flows  ..........................  10
   9.  Security Considerations  .....................................  11
   10.  IANA Considerations  .........................................  12
   11.  References  ..................................................  13

1.  Introduction

   The rapid deployment of AI agents across organizational boundaries
   has created an urgent need for collaborative learning mechanisms
   that preserve privacy while enabling knowledge sharing.
   Traditional centralized machine learning approaches require data
   consolidation, which violates privacy regulations and
   organizational policies in multi-tenant environments where
   competing organizations must collaborate. Federated learning
   addresses data centralization concerns but existing protocols lack
   comprehensive privacy guarantees necessary for cross-
   organizational AI agent deployments. Current federated learning
   frameworks are vulnerable to gradient inversion attacks [draft-
   kale-agntcy-federated-privacy], membership inference attacks, and
   cross-tenant data reconstruction, making them unsuitable for
   privacy-sensitive multi-tenant agent learning scenarios.

   Multi-tenant federated agent learning presents unique challenges
   beyond traditional federated learning contexts. AI agents often
   process behavioral data, decision patterns, and strategic
   information that require stronger privacy protections than
   conventional machine learning datasets. The heterogeneous nature
   of agent types across organizations complicates model aggregation
   while maintaining meaningful learning outcomes. Existing privacy-
   preserving techniques such as differential privacy and secure
   aggregation, when applied independently, fail to provide
   comprehensive protection against sophisticated attacks targeting
   federated agent systems. Furthermore, regulatory compliance
   requirements such as GDPR's right to erasure and data processing
   transparency mandates necessitate cryptographic verification
   mechanisms that current federated learning protocols do not
   provide.

   This specification introduces the Privacy-Preserving Agent
   Learning Protocol (PPALP) to address critical privacy gaps in
   federated agent architectures while enabling secure cross-
   organizational AI collaboration. PPALP combines differential
   privacy mechanisms specifically calibrated for agent model
   parameters, secure multi-party computation for cross-tenant
   aggregation, and zero-knowledge proofs for cryptographic
   verification of privacy compliance. The protocol builds upon the
   Privacy-Preserving Reference Architecture defined in [draft-kale-
   agntcy-federated-privacy] and extends federated averaging
   mechanisms to support heterogeneous agent types with formal
   privacy guarantees. PPALP integrates with existing federated
   learning frameworks through standardized APIs while providing
   backward compatibility for legacy deployments.

   The protocol's contribution to AI agent standardization includes
   formal privacy accounting mechanisms that track privacy budget
   consumption across multiple learning rounds, tenant isolation
   enforcement that prevents cross-organizational information
   leakage, and cryptographic audit trails that enable regulatory
   compliance verification. PPALP addresses the critical need for
   standardized privacy-preserving protocols in federated agent
   learning by providing implementable solutions that balance
   collaborative learning benefits with stringent privacy
   requirements. The specification defines concrete message formats,
   communication flows, and security mechanisms necessary for
   production deployment of privacy-preserving federated agent
   learning systems in multi-tenant environments where traditional
   approaches fail to provide adequate protection.

2.  Terminology

   This document defines terminology for privacy-preserving federated
   learning protocols in multi-tenant environments. The key words
   "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD",
   "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   Agent: An autonomous software entity capable of learning and
   adapting its behavior through machine learning mechanisms, as
   defined in [draft-berlinai-vera]. In the context of this
   specification, agents participate in federated learning to improve
   their models collaboratively.

   Federated Agent Learning: A distributed machine learning paradigm
   where multiple AI agents collaboratively train shared models
   without centralizing their training data. Each agent maintains
   local data while contributing to global model improvement through
   privacy-preserving parameter sharing mechanisms.

   Privacy Budget: A quantitative measure of the privacy cost
   incurred by releasing information about a dataset through
   differential privacy mechanisms. The privacy budget, typically
   denoted as epsilon (ε), bounds the maximum privacy loss across all
   learning iterations and MUST be carefully managed to maintain
   formal privacy guarantees throughout the federated learning
   process.

   Differential Privacy: A mathematical framework that provides
   formal privacy guarantees by adding calibrated noise to
   computation outputs. In federated agent learning, differential
   privacy mechanisms ensure that the participation of any individual
   data point cannot be inferred from the released model parameters
   or gradients.

   Tenant: An organizational entity participating in federated
   learning with its own collection of agents, data, and privacy
   requirements. Tenants represent distinct administrative domains
   that require isolation guarantees while participating in
   collaborative learning protocols.

   Tenant Isolation: The property that agent learning activities of
   one organizational tenant cannot infer private information about
   another tenant's data or models. Tenant isolation MUST be
   maintained through cryptographic mechanisms and differential
   privacy guarantees to prevent cross-organizational data leakage.

   Secure Aggregation: A cryptographic protocol that allows
   computation of aggregate statistics over distributed data without
   revealing individual contributions. Secure aggregation protocols
   typically employ techniques such as homomorphic encryption or
   secure multiparty computation to ensure that only the aggregated
   result is revealed to participants.

   Federated Coordinator: The central entity responsible for
   orchestrating privacy-preserving model aggregation across multiple
   tenants in federated learning. The coordinator manages
   communication flows, enforces privacy policies, and coordinates
   the secure aggregation process while maintaining tenant isolation
   guarantees.

   Privacy Compliance Verifier: A system component that
   cryptographically verifies adherence to differential privacy
   guarantees and tenant isolation requirements. The verifier employs
   zero-knowledge proofs and audit mechanisms to ensure that all
   participants comply with established privacy policies without
   revealing sensitive information about their contributions.

   Gradient Inversion Attack: An attack where an adversary attempts
   to reconstruct training data from shared gradient information in
   federated learning. Such attacks exploit the information leakage
   inherent in gradient sharing and represent a primary threat that
   privacy-preserving protocols MUST defend against.

   Membership Inference Attack: An attack technique where an
   adversary attempts to determine whether a specific data point was
   used in training a machine learning model. In federated settings,
   these attacks can be used to infer information about individual
   agents' training data across tenant boundaries.

   Privacy Accounting: The process of tracking and managing privacy
   budget consumption across multiple learning rounds and operations.
   Privacy accounting mechanisms MUST ensure that the cumulative
   privacy loss remains within acceptable bounds throughout the
   entire federated learning process.

   Homomorphic Encryption: A cryptographic technique that allows
   computation on encrypted data without decrypting it first. In the
   context of secure aggregation, homomorphic encryption enables the
   computation of aggregate model parameters while keeping individual
   contributions encrypted.

   Zero-Knowledge Proof: A cryptographic protocol that allows one
   party to prove knowledge of information to another party without
   revealing the information itself. Zero-knowledge proofs are used
   in privacy compliance verification to demonstrate adherence to
   privacy requirements without exposing sensitive details about the
   verification process.

3.  Problem Statement

   Federated agent learning in multi-tenant environments faces
   fundamental privacy leakage risks that existing protocols fail to
   adequately address. The core problem arises when AI agents from
   different organizations (tenants) participate in collaborative
   learning while sharing gradient updates or model parameters that
   inadvertently reveal sensitive information about their private
   training data. Unlike traditional federated learning scenarios
   with homogeneous participants, multi-tenant environments amplify
   privacy risks due to potentially adversarial relationships between
   tenants and the heterogeneous nature of agent architectures across
   organizational boundaries.

   The threat model for privacy-preserving federated agent learning
   encompasses three primary attack vectors that adversaries MAY
   exploit to compromise tenant data privacy. First, gradient
   inversion attacks allow malicious participants to reconstruct
   approximations of private training samples from shared gradient
   information, particularly effective when batch sizes are small or
   when attackers can observe multiple training rounds. Second,
   membership inference attacks enable adversaries to determine
   whether specific data points were used in another tenant's
   training dataset by analyzing model behavior or parameter updates.
   Third, cross-tenant data reconstruction attacks leverage
   correlations between multiple tenants' shared information to infer
   sensitive attributes about datasets belonging to other
   organizational participants, violating tenant isolation
   requirements.

   The privacy leakage problem is quantified through formal privacy
   metrics that MUST account for composition across multiple learning
   rounds and tenant interactions. Let ε represent the privacy budget
   expended per tenant per learning round, and δ represent the
   probability of privacy violation. The cumulative privacy cost
   grows with each parameter sharing event, and without proper
   privacy accounting mechanisms, the total privacy loss (εtotal,
   δtotal) MAY exceed acceptable thresholds defined by organizational
   privacy policies. Traditional differential privacy mechanisms
   designed for single-organization scenarios provide insufficient
   guarantees in multi-tenant federated settings where privacy
   budgets must be allocated across tenant boundaries and learning
   rounds while maintaining utility for collaborative model
   improvement.

   Building upon the AI Agent Structured Threat Model framework
   defined in [draft-berlinai-vera], federated agent learning
   environments face additional risks including compromised privacy
   compliance verifiers that MAY provide false attestations of
   differential privacy adherence, tenant gateway failures that could
   leak cross-tenant information, and coordination attacks where
   multiple tenants collude to circumvent privacy protections. The
   threat model assumes honest-but-curious federated coordinators who
   follow protocol specifications but MAY attempt to infer private
   information from aggregated data, and potentially malicious
   tenants who MAY deviate from prescribed privacy-preserving
   mechanisms to gain competitive advantages through unauthorized
   data access.

   The problem statement also addresses the challenge of maintaining
   privacy guarantees while supporting heterogeneous agent
   architectures with varying computational capabilities, privacy
   requirements, and trust assumptions. Agents operating under
   different privacy regulations (such as GDPR, CCPA, or sector-
   specific requirements) MUST be able to participate in federated
   learning while maintaining compliance with their respective
   privacy obligations. The protocol MUST provide mechanisms for
   cryptographic verification of privacy compliance, audit trails for
   regulatory reporting, and graceful degradation when privacy
   budgets are exhausted, ensuring that collaborative learning can
   continue while respecting individual tenant privacy constraints
   and maintaining the overall utility of the federated learning
   process.

4.  Privacy-Preserving Agent Learning Architecture

   This section defines the Privacy-Preserving Agent Learning
   Protocol (PPALP) architecture, which integrates differential
   privacy, secure aggregation, and zero-knowledge proofs to enable
   federated learning across organizational boundaries while
   maintaining formal privacy guarantees. The architecture extends
   the Privacy-Preserving Reference Architecture framework [draft-
   kale-agntcy-federated-privacy] to specifically address agent
   learning scenarios in multi-tenant environments. PPALP employs a
   layered approach where privacy mechanisms are enforced at multiple
   architectural levels to provide defense-in-depth protection
   against information leakage.

   Tenant A         Tenant B         Tenant C
   +--------+      +--------+      +--------+
   | Agent  |      | Agent  |      | Agent  |
   | Local  |      | Local  |      | Local  |
   | Model  |      | Model  |      | Model  |
   +---+----+      +---+----+      +---+----+
       |               |               |
       v               v               v
   +---+----+      +---+----+      +---+----+
   |  DP    |      |  DP    |      |  DP    |
   | Noise  |      | Noise  |      | Noise  |
   +---+----+      +---+----+      +---+----+
       |               |               |
       +-------+-------+-------+------+
               |               |
          +----+-----+   +----+------+
          |  Secure  |   | Privacy   |
          |  Aggre-  |   | Compliance|
          |  gation  |   |  Verifier |
          +----------+   +-----------+

      Figure 1: PPALP Federated Learning Architecture

   The PPALP architecture defines four primary component roles that
   collectively ensure privacy-preserving federated learning
   operations. The Federated Coordinator serves as the central
   orchestration entity responsible for managing global model state,
   coordinating aggregation rounds, and maintaining privacy budget
   allocations across all participating tenants. Tenant Gateways act
   as intermediary control plane entities, extending the Agent
   Gateway Control Plane concept [draft-li-dmsc-mcps-agw] to enforce
   tenant-specific privacy policies and manage agent authentication
   within organizational boundaries. Privacy Compliance Verifiers
   operate as independent auditing entities that cryptographically
   verify adherence to differential privacy guarantees and tenant
   isolation requirements through zero-knowledge proofs.
   Participating Agents represent the distributed learning entities
   that contribute model updates while consuming privacy budget
   allocated by their respective tenants.

   The architectural design enforces strict tenant isolation through
   cryptographic boundaries and policy enforcement mechanisms. Each
   tenant maintains an isolated privacy budget that cannot be
   influenced by other tenants' learning activities, ensuring that
   cross-tenant privacy guarantees remain intact regardless of
   adversarial behavior by other participants. Tenant Gateways
   implement the Federated Policy Enforcement framework [draft-cui-
   dmsc-agent-cdi] to harmonize local privacy policies with global
   federated learning requirements while preventing policy conflicts
   that could compromise privacy guarantees. All inter-component
   communications MUST use TLS 1.3 [RFC8446] with mutual
   authentication based on X.509 certificates [RFC5280] to prevent
   network-level attacks.

   The protocol supports heterogeneous agent types through a
   standardized privacy-preserving model update interface that
   abstracts underlying model architectures while ensuring consistent
   privacy accounting. Agents MUST register their learning
   capabilities and privacy requirements with their tenant's gateway,
   which negotiates appropriate differential privacy parameters with
   the Federated Coordinator. The architecture maintains backward
   compatibility with existing federated learning frameworks by
   implementing standard aggregation interfaces that can be secured
   through the PPALP privacy mechanisms without requiring
   modifications to existing agent implementations.

   Privacy accounting occurs at multiple architectural layers to
   provide comprehensive protection against composition attacks and
   privacy budget exhaustion. The Federated Coordinator maintains
   global privacy budget state and implements composition theorems to
   track cumulative privacy expenditure across learning rounds.
   Tenant Gateways enforce local privacy policies and MAY implement
   additional privacy amplification techniques such as subsampling to
   reduce privacy costs for their constituent agents. Privacy
   Compliance Verifiers continuously monitor privacy budget
   consumption and generate cryptographic proofs of compliance that
   can be audited by external parties without revealing sensitive
   learning parameters or model information.

5.  Differential Privacy for Agent Model Updates

   This section specifies the differential privacy mechanisms that
   MUST be applied to agent model parameter updates to provide formal
   privacy guarantees in multi-tenant federated learning
   environments. All participating agents MUST implement differential
   privacy mechanisms that satisfy ε-differential privacy for a pre-
   negotiated privacy parameter ε, where smaller values of ε provide
   stronger privacy guarantees but may reduce model utility. The
   differential privacy implementation MUST use the Gaussian
   mechanism for continuous-valued parameters and the exponential
   mechanism for discrete-valued selections, calibrated to the global
   sensitivity of the learning algorithm as defined in [draft-kale-
   agntcy-federated-privacy].

   Privacy budget allocation MUST be managed across multiple learning
   rounds to prevent privacy depletion over time. Each tenant MUST
   maintain a privacy budget tracker that accounts for the cumulative
   privacy cost across all federated learning rounds, where the total
   privacy cost is bounded by composition theorems. The system MUST
   implement advanced composition using the moments accountant method
   or Rényi differential privacy to achieve tighter privacy bounds
   than basic composition would allow. Privacy budgets SHOULD be
   allocated proportionally to the expected utility gain from each
   learning round, with mechanisms for tenants to reserve budget for
   future high-priority learning tasks.

   Noise calibration for model parameter updates MUST be performed
   based on the L2 sensitivity of the gradient computation, which is
   bounded through gradient clipping techniques. Before applying
   differential privacy noise, gradients MUST be clipped to a maximum
   norm C, where C is negotiated during protocol initialization based
   on the trade-off between privacy, utility, and convergence
   requirements. The Gaussian noise added to each parameter update
   MUST have variance σ² = 2C²ln(1.25/δ)/ε² for (ε,δ)-differential
   privacy, where δ represents the probability of privacy failure and
   MUST be set to be cryptographically negligible (typically δ ≤
   10⁻⁶).

   The protocol MUST maintain detailed privacy accounting across
   learning rounds using a standardized privacy ledger format that
   records the privacy cost of each operation. This ledger MUST track
   per-tenant privacy expenditure, cross-tenant privacy interactions,
   and cumulative privacy debt according to the composition theorems.
   The privacy accounting system MUST provide cryptographic proofs of
   privacy budget compliance that can be verified by Privacy
   Compliance Verifiers as specified in Section 7. When a tenant's
   privacy budget is exhausted, the system MUST either terminate that
   tenant's participation or implement privacy budget renewal
   mechanisms with appropriate cooling-off periods to prevent privacy
   budget laundering attacks.

   The differential privacy implementation MUST be compatible with
   the secure aggregation protocols defined in Section 6, ensuring
   that privacy guarantees are maintained even when combined with
   cryptographic operations. The noise addition process MUST occur
   before any cryptographic encoding for secure aggregation, and the
   protocol MUST verify that the combination of differential privacy
   and secure aggregation does not introduce additional privacy
   vulnerabilities through cryptographic side channels or timing
   attacks. All differential privacy parameters, including ε, δ, and
   clipping bounds, MUST be negotiated during the protocol handshake
   phase and cryptographically committed to prevent manipulation
   during the learning process.

6.  Secure Multi-Tenant Federated Aggregation

   This section defines the cryptographic protocols that enable
   secure aggregation of model parameters across tenant boundaries
   while preserving privacy guarantees established in Section 5. The
   secure aggregation protocol MUST ensure that no individual tenant
   can learn information about other tenants' model updates beyond
   what is revealed by the final aggregated result. The protocol
   combines homomorphic encryption with secure multiparty computation
   techniques to achieve this property while maintaining
   computational efficiency suitable for practical federated learning
   deployments.

   Agent_i         Aggregator       Verifier
     |                |                |
     |--local grad--->|                |
     |  + DP noise    |                |
     |                |                |
     |  [...N agents contribute...]    |
     |                |                |
     |                |--aggregate---->|
     |                |  model update  |
     |                |                |
     |                |<--ZK proof-----|
     |                |  compliance    |
     |<--global upd---|                |
     |                |                |

      Figure 2: Privacy-Preserving Aggregation Flow

   The Multi-Tenant Federated Averaging mechanism serves as the
   foundation for secure parameter aggregation. Each tenant's Privacy
   Compliance Verifier MUST encrypt their differentially-private
   model updates using a threshold homomorphic encryption scheme
   before transmission to the Federated Coordinator. The encryption
   MUST use a (t,n)-threshold scheme where t represents the minimum
   number of tenants required for decryption and n represents the
   total number of participating tenants. This ensures that no single
   tenant or coordinator can decrypt individual updates, while still
   enabling computation over encrypted parameters. The homomorphic
   encryption scheme SHOULD support both addition and limited
   multiplication operations to accommodate various neural network
   architectures commonly used in agent learning systems.

   The secure multiparty computation protocol proceeds in three
   phases: commitment, computation, and verification. During the
   commitment phase, each tenant generates a cryptographic commitment
   to their encrypted model update using a computationally-hiding and
   perfectly-binding commitment scheme. The Federated Coordinator
   collects all commitments and broadcasts them to participating
   tenants for verification. In the computation phase, tenants
   collaboratively execute a secure aggregation protocol that
   computes the weighted average of model parameters without
   revealing individual contributions. The protocol MUST be secure
   against up to t-1 malicious participants and SHOULD provide
   robustness against participant dropout through verifiable secret
   sharing techniques.

   Verification mechanisms ensure correctness of the aggregation
   process and detect potential manipulation attempts. Each
   participating tenant MUST verify the integrity of the aggregation
   using zero-knowledge proofs that demonstrate correct execution of
   the secure computation protocol. The ZKP-based Agent Attestation
   mechanism enables tenants to prove their compliance with the
   differential privacy requirements specified in Section 5 without
   revealing their actual noise parameters or model updates. These
   proofs MUST be verified by the Federated Coordinator and recorded
   in an immutable audit trail as specified in Section 7.
   Additionally, the protocol includes range proofs to verify that
   model parameter updates fall within expected bounds, preventing
   gradient manipulation attacks that could compromise model
   convergence or extract private information.

   The aggregation protocol MUST support heterogeneous agent types by
   normalizing model parameters to a common representation before
   encryption and aggregation. Tenants with different model
   architectures encode their updates using a standardized parameter
   mapping that preserves semantic meaning while enabling secure
   computation. The protocol SHOULD maintain backward compatibility
   with existing federated learning frameworks by providing adapter
   layers that translate between PPALP message formats defined in
   Section 8 and legacy federated learning APIs. Performance
   optimizations including parameter compression, selective
   aggregation of model layers, and asynchronous update mechanisms
   MAY be implemented provided they do not compromise the privacy
   guarantees established by the differential privacy and secure
   aggregation components.

7.  Privacy Compliance and Verification

   Privacy compliance in federated agent learning environments
   requires cryptographic mechanisms that provide verifiable proof of
   adherence to differential privacy guarantees while maintaining
   audit capabilities for regulatory requirements. This section
   defines the Privacy Compliance and Verification framework that
   enables tenants to demonstrate privacy compliance without
   revealing sensitive implementation details or compromising the
   privacy of their learning processes. The framework combines zero-
   knowledge proofs, selective disclosure attestations, and
   cryptographic erasure mechanisms to provide comprehensive privacy
   verification capabilities.

   The Privacy Compliance Verifier MUST implement zero-knowledge
   proof protocols that allow federated agents to demonstrate
   differential privacy adherence without revealing actual noise
   values, data distributions, or model parameters. Following the SD-
   JWT Privacy-Preserving Trust Proof pattern from [draft-drake-
   email-tpm-attestation], the verifier SHALL use Selective
   Disclosure JWT mechanisms [RFC7519] to enable privacy-preserving
   attestation of compliance properties. Each participating agent
   MUST generate cryptographic proofs that demonstrate: (1) proper
   calibration of differential privacy noise according to the
   allocated privacy budget, (2) correct implementation of the
   Gaussian mechanism or equivalent noise generation, and (3)
   adherence to composition theorems across multiple learning rounds.
   These proofs MUST be constructed using non-interactive zero-
   knowledge proof systems such as zk-SNARKs or zk-STARKs that
   provide succinctness and efficient verification.

   Audit trail mechanisms MUST maintain cryptographically verifiable
   logs of all privacy-relevant operations while preserving the
   confidentiality of sensitive learning data. The audit system SHALL
   record privacy budget expenditures, aggregation operations, and
   tenant isolation boundary enforcement without logging actual model
   parameters or gradient values. Each audit entry MUST include a
   timestamp, tenant identifier, privacy budget allocation, and
   cryptographic hash of the differential privacy parameters used.
   The audit trail MUST support selective disclosure to authorized
   auditors while preventing unauthorized access to privacy-sensitive
   operational details. Privacy budget accounting MUST be maintained
   across all learning rounds with cryptographic commitments that
   prevent double-spending of privacy allocations.

   Cryptographic erasure capabilities MUST be implemented to address
   regulatory requirements for data deletion and agent memory cleanup
   as specified in [draft-gaikwad-aps-profile]. The system SHALL
   support cryptographic erasure of agent memory associated with
   specific user or agent identities through key destruction
   mechanisms that render associated encrypted data permanently
   inaccessible. When a deletion request is received, the Privacy
   Compliance Verifier MUST coordinate the erasure of all federated
   learning artifacts associated with the specified identity,
   including gradient histories, model update records, and privacy
   budget allocations. The erasure process MUST generate
   cryptographic proofs of deletion that demonstrate the irreversible
   destruction of the requested data across all participating
   federated nodes.

   Privacy compliance verification MUST support Privacy-Respecting
   Capability Attestation mechanisms that enable verification of
   agent privacy capabilities without revealing implementation-
   specific details. Following patterns from [draft-huang-rats-
   agentic-eat-cap-attest], agents MUST be able to attest to their
   differential privacy implementation capabilities, secure
   aggregation support, and tenant isolation enforcement without
   disclosing proprietary algorithms or security configurations. The
   attestation process SHALL use hardware-backed trust anchors where
   available, with fallback to software-based attestation for
   environments without trusted execution capabilities. All privacy
   compliance proofs and attestations MUST be timestamped and include
   validity periods to prevent replay attacks and ensure freshness of
   compliance claims.

8.  Protocol Message Formats and Flows

   This section defines the concrete message formats, communication
   flows, and API definitions for the Privacy-Preserving Agent
   Learning Protocol (PPALP). All message exchanges MUST use JSON
   format as specified in [RFC8259] with UTF-8 encoding. Protocol
   implementations MUST support TLS 1.3 [RFC8446] for transport
   security and MUST authenticate using OAuth 2.0 [RFC6749] bearer
   tokens or X.509 certificates [RFC5280]. The protocol defines four
   primary message categories: initialization messages for
   establishing privacy parameters, update messages for transmitting
   differentially private model parameters, aggregation messages for
   secure multi-tenant computation, and verification messages for
   privacy compliance validation.

   The protocol message structure follows a common envelope format
   with mandatory header fields. Each message MUST include a
   "version" field specifying the PPALP protocol version, a "type"
   field indicating the message category, a "tenantid" field for
   tenant isolation enforcement, and a "timestamp" field using ISO
   8601 format. Messages involving privacy budget allocation MUST
   include a "privacybudget" object containing epsilon and delta
   values as defined in Section 5. Security-critical messages such as
   model updates and aggregation requests MUST include a "signature"
   field containing a JWS signature [RFC7519] for message
   authentication and integrity verification.

   The federated learning session begins with a Privacy Parameter
   Negotiation flow initiated by the Federated Coordinator. The
   coordinator sends a "privacyinit" message specifying the global
   privacy budget, differential privacy mechanism, and aggregation
   method. Each Tenant Gateway responds with a "privacyaccept"
   message confirming participation and local privacy constraints.
   Model update flows use "modelupdate" messages containing encrypted
   differentially private gradients, privacy proof attestations, and
   secure aggregation shares. The Federated Coordinator processes
   these through "aggregationrequest" and "aggregation_response"
   messages that implement the secure multi-tenant aggregation
   protocol defined in Section 6.

   Privacy compliance verification occurs through dedicated message
   flows involving the Privacy Compliance Verifier component. Agents
   MUST send "privacyproof" messages containing zero-knowledge proofs
   of differential privacy adherence for each model update. The
   verifier responds with "compliancestatus" messages indicating
   verification success or failure with specific error codes. Audit
   trail generation uses "audit_log" messages that record privacy
   budget consumption, aggregation participation, and compliance
   verification events. All audit messages MUST be cryptographically
   signed and include tamper-evident sequence numbers for regulatory
   compliance.

   The protocol defines standardized error handling through HTTP
   status codes [RFC9110] and structured error responses. Privacy
   budget exhaustion MUST return HTTP 429 with a
   "privacybudgetexceeded" error code and remaining budget
   information. Tenant isolation violations MUST return HTTP 403 with
   detailed violation descriptions for security monitoring.
   Implementation-specific extensions MAY add custom message fields
   using the "extensions" object within the message envelope,
   provided they do not compromise privacy guarantees or tenant
   isolation requirements. Reference implementations MUST provide
   JSON Schema validation files and OpenAPI specifications for all
   message formats to ensure interoperability across different PPALP
   implementations.

   Example message flows demonstrate typical protocol interactions
   including session establishment, multi-round federated learning
   with privacy budget management, and graceful session termination
   with privacy compliance verification. The appendix includes
   complete JSON Schema definitions for all message types, sample
   message exchanges showing proper privacy parameter negotiation,
   and error handling examples covering common failure scenarios such
   as aggregation timeouts and privacy proof validation failures.

9.  Security Considerations

   PPALP addresses multiple security threats present in multi-tenant
   federated learning environments through defense-in-depth
   mechanisms. The protocol MUST implement protections against
   gradient inversion attacks [RFC8446] by ensuring differential
   privacy noise addition occurs before any parameter sharing, with
   privacy budgets calculated according to composition theorems
   defined in Section 5. Membership inference attacks are mitigated
   through tenant isolation enforcement, where each tenant's Privacy
   Compliance Verifier cryptographically verifies that cross-tenant
   information leakage remains below specified epsilon-delta bounds.
   Model poisoning attacks are addressed through secure aggregation
   protocols that include Byzantine-fault-tolerant mechanisms and
   zero-knowledge proofs of honest participation.

   Key management in PPALP follows a hierarchical trust model where
   the Federated Coordinator maintains root cryptographic
   credentials, while each tenant operates independent key
   hierarchies for their agent populations. Tenant Gateways MUST
   establish mutually authenticated channels using TLS 1.3 [RFC8446]
   with certificate pinning, and rotate encryption keys according to
   the privacy budget consumption rate to ensure forward secrecy. The
   protocol supports both pre-shared keys for closed consortium
   deployments and public key infrastructure integration [RFC5280]
   for open multi-tenant scenarios. Cross-tenant cryptographic
   isolation MUST be maintained such that compromise of one tenant's
   keys cannot affect the privacy guarantees of other tenants'
   learning processes.

   Operational security considerations for multi-tenant deployments
   include audit logging of all privacy budget expenditures, secure
   deletion mechanisms for ephemeral cryptographic material, and
   side-channel attack mitigation. The protocol MUST log all
   differential privacy parameter selections, aggregation events, and
   privacy compliance verification results to enable post-hoc
   security analysis while ensuring these logs themselves do not leak
   private information. Network-level protections SHOULD include
   traffic analysis resistance through padding and timing
   obfuscation, particularly for Agent Gateway intercommunication as
   specified in [draft-han-rtgwg-agent-gateway-intercomm-framework].
   Deployments MUST implement secure boot and attestation mechanisms
   for Privacy Compliance Verifiers to prevent tampering with privacy
   enforcement logic.

   The protocol provides resistance to collusion attacks through
   cryptographic commitment schemes and verifiable privacy budget
   accounting across multiple learning rounds. When tenant subsets
   attempt to correlate their local model updates to infer
   information about non-colluding tenants, the secure aggregation
   protocol ensures that only differentially private aggregate
   statistics are revealed. However, the protocol assumes honest-but-
   curious adversaries and MAY NOT provide protection against active
   adversaries who deviate from the protocol specification.
   Deployments requiring protection against malicious adversaries
   SHOULD implement additional verification mechanisms and consider
   reduced privacy parameters to account for adaptive attacks over
   multiple learning epochs.

10.  IANA Considerations

   This document defines new protocol elements that require
   registration with the Internet Assigned Numbers Authority (IANA)
   to ensure interoperability and avoid conflicts with existing
   protocols. The Privacy-Preserving Agent Learning Protocol (PPALP)
   introduces novel message types, error codes, and protocol
   identifiers that extend existing federated learning and privacy-
   preserving computation frameworks as defined in [draft-kale-
   agntcy-federated-privacy].

   IANA SHALL establish a new registry titled "Privacy-Preserving
   Agent Learning Protocol (PPALP) Parameters" under the "AI Agent
   Protocol Parameters" registry group. This registry SHALL contain
   three sub-registries: "PPALP Message Types", "PPALP Error Codes",
   and "PPALP Privacy Compliance Identifiers". The PPALP Message
   Types registry SHALL include entries for differential privacy
   update messages (DPUPDATE), secure aggregation requests
   (SECAGGREQ), secure aggregation responses (SECAGGRESP), privacy
   budget allocation messages (PRIVBUDGET), tenant isolation
   verification messages (TENANTVERIFY), and zero-knowledge proof
   submission messages (ZKPSUBMIT). Each message type SHALL be
   assigned a unique 16-bit identifier following the allocation
   policies specified in [RFC8259] for JSON-based protocol
   extensions.

   The PPALP Error Codes registry SHALL define error conditions
   specific to privacy-preserving federated learning operations.
   Required error codes include PRIVACYBUDGETEXCEEDED (4001),
   TENANTISOLATIONVIOLATION (4002),
   DIFFERENTIALPRIVACYVERIFICATIONFAILED (4003),
   SECUREAGGREGATIONTIMEOUT (4004), INSUFFICIENTPRIVACYPARAMETERS
   (4005), and ZKPVERIFICATION_FAILED (4006). These error codes
   extend the HTTP status code space as defined in [RFC9110] and
   SHALL follow the expert review allocation policy with designated
   experts appointed from the privacy-preserving computation and
   federated learning communities.

   The PPALP Privacy Compliance Identifiers registry SHALL contain
   standardized identifiers for privacy mechanisms, verification
   schemes, and compliance frameworks used within the protocol. This
   registry SHALL include identifiers for differential privacy
   mechanisms (DPGAUSSIAN, DPLAPLACE), secure aggregation protocols
   (SECAGGBFV, SECAGGCKKS), and zero-knowledge proof systems
   (ZKPBULLETPROOFS, ZKPPLONK). Registration policies for this
   registry SHALL follow the Specification Required policy as defined
   in [RFC2119], with specifications requiring formal privacy
   analysis and security proofs. Updates to these registries SHALL be
   coordinated with the broader AI agent protocol standardization
   efforts referenced in [draft-berlinai-vera] to ensure consistency
   across related privacy-preserving agent communication protocols.

11.  References

11.1.  Normative References

   [RFC 2119]
         RFC 2119

   [RFC 8174]
         RFC 8174

   [RFC 8259]
         RFC 8259

   [RFC 7519]
         RFC 7519

   [RFC 8446]
         RFC 8446

   [draft-kale-agntcy-federated-privacy]
         draft-kale-agntcy-federated-privacy

11.2.  Informative References

   [RFC 6749]
         RFC 6749

   [RFC 9110]
         RFC 9110

   [RFC 5280]
         RFC 5280

   [draft-berlinai-vera]
         draft-berlinai-vera

   [draft-huang-acme-scalable-agent-enrollment]
         draft-huang-acme-scalable-agent-enrollment

   [draft-messous-eat-ai]
         draft-messous-eat-ai

   [draft-li-dmsc-mcps-agw]
         draft-li-dmsc-mcps-agw

   [draft-gaikwad-aps-profile]
         draft-gaikwad-aps-profile

   [draft-drake-email-tpm-attestation]
         draft-drake-email-tpm-attestation

   [draft-huang-rats-agentic-eat-cap-attest]
         draft-huang-rats-agentic-eat-cap-attest


Author's Address

   Generated by IETF Draft Analyzer
   Family: agent-ecosystem
   2026-03-04
