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---
title: "Federated Agent Learning Privacy and Cross-Protocol Migration"
abbrev: "Agent Federation Privacy"
category: std
docname: draft-nennemann-agent-federation-privacy-00
submissiontype: IETF
number:
date:
v: 3
area: "OPS"
workgroup: "NMOP"
keyword:
- federated learning
- agent privacy
- agent migration
- cross-protocol
- differential privacy
author:
-
fullname: Christian Nennemann
organization: Independent Researcher
email: ietf@nennemann.de
normative:
RFC2119:
RFC8174:
RFC7519:
RFC7515:
RFC9110:
I-D.nennemann-wimse-ect:
title: "Execution Context Tokens for Distributed Agentic Workflows"
target: https://datatracker.ietf.org/doc/draft-nennemann-wimse-ect/
I-D.nennemann-agent-dag-hitl-safety:
title: "Agent Context Policy Token: DAG Delegation with Human Override"
target: https://datatracker.ietf.org/doc/draft-nennemann-agent-dag-hitl-safety/
informative:
I-D.nennemann-agent-gap-analysis:
title: "Gap Analysis of IETF Agent-Related Drafts"
target: https://datatracker.ietf.org/doc/draft-nennemann-agent-gap-analysis/
--- abstract
This document defines privacy-preserving protocols for federated
agent learning across organizational boundaries and standardized
mechanisms for agent migration between protocols, domains, and
infrastructure providers while maintaining state and identity
continuity. Federated learning enables multiple agent deployments
to collaboratively improve without sharing raw data, but requires
formal privacy guarantees to prevent data leakage between
participants. Cross-protocol migration enables agents to move
between environments while preserving operational state and
cryptographic identity through Execution Context Tokens (ECTs).
--- middle
# Introduction
As AI agents become integral to enterprise workflows, two
capabilities emerge as critical yet underspecified: collaborative
learning across organizational boundaries and seamless migration
between protocol environments.
This document addresses Gap 5 (Federated Learning Privacy) and
Gap 8 (Cross-Protocol Migration) as identified in
{{I-D.nennemann-agent-gap-analysis}}.
Gap 5 concerns the absence of privacy-preserving protocols for
federated agent learning. As agents learn and improve through
federation, data leakage between participants must be prevented.
Current federated learning research provides theoretical
foundations, but no IETF-standard protocol exists that integrates
differential privacy, secure aggregation, and privacy budget
enforcement into agent communication frameworks.
Gap 8 concerns the lack of standardized mechanisms for agent
migration between protocols, domains, and infrastructure providers.
As agents need to move between environments -- whether for load
balancing, disaster recovery, or organizational restructuring --
state and identity must transfer safely. Without a migration
protocol, agents lose context, learned parameters, and
cryptographic identity when changing environments.
This document builds on the Execution Context Token (ECT)
framework {{I-D.nennemann-wimse-ect}} to provide cryptographic
audit trails for both federated learning rounds and migration
events, and on the Agent Context Policy Token
{{I-D.nennemann-agent-dag-hitl-safety}} to enforce privacy and
migration policies within delegation DAGs.
# Terminology
{::boilerplate bcp14-tagged}
The following terms are used in this document:
Federated Learning:
: A machine learning approach where multiple participants
collaboratively train a model without sharing raw data, instead
exchanging model updates (gradients or parameters).
Differential Privacy:
: A mathematical framework providing formal guarantees that the
output of a computation does not reveal whether any individual
data point was included in the input, parameterized by epsilon
and delta.
Secure Aggregation:
: A cryptographic protocol enabling a server to compute the sum
of participant updates without learning any individual update.
Privacy Budget:
: A cumulative bound (epsilon) on the total privacy loss incurred
across multiple rounds of federated learning, enforced to prevent
gradual information leakage.
Data Leakage:
: The unintended exposure of private training data through model
updates, inference attacks, or side channels during federated
learning.
Agent Migration:
: The process of transferring an agent's operational state,
identity, and capabilities from one protocol environment, domain,
or infrastructure provider to another.
State Transfer:
: The serialization, transmission, and deserialization of an
agent's internal state during migration, including context,
memory, learned parameters, and active tasks.
Identity Continuity:
: The property that an agent's cryptographic identity (e.g., SPIFFE
ID and associated ECT chain) remains verifiable across migration
boundaries.
Protocol Bridge:
: A component that translates agent communication between different
protocols (e.g., A2A to MCP), maintaining semantic equivalence
of messages and state.
Migration Handoff:
: The coordinated process by which the source environment transfers
responsibility for an agent to the destination environment,
including state transfer and identity re-attestation.
# Federated Agent Learning Privacy
## Federated Learning Architecture for Agents
Federated learning for agents follows a topology where participant
agents contribute model updates to an aggregation function without
exposing their local training data.
~~~
+---------------------------------------------------+
| Federation Topology |
| |
| Star: Ring: Hierarchical: |
| |
| [Agg] A1--A2 [Root Agg] |
| / | \ | | / \ |
| A1 A2 A3 A3--A4 [Sub-Agg] [Sub-Agg] |
| / \ / \ |
| A1 A2 A3 A4 |
| |
+---------------------------------------------------+
[Agg] = Aggregation Server
A1..A4 = Participant Agents
Data flow (Star topology):
A1 ---local_update---> [Agg]
A2 ---local_update---> [Agg]
A3 ---local_update---> [Agg]
[Agg] computes aggregate
A1 <--global_model--- [Agg]
A2 <--global_model--- [Agg]
A3 <--global_model--- [Agg]
~~~
{: #fig-federation-arch title="Federated Learning Topologies for Agents"}
Three topologies are defined:
Star Topology:
: A central aggregation server receives updates from all
participant agents and distributes the aggregated model. This
is the simplest topology but creates a single point of trust.
Ring Topology:
: Participant agents pass updates around a ring, each adding its
own contribution before forwarding. This eliminates the central
server but increases latency.
Hierarchical Topology:
: Sub-aggregation servers collect updates from subsets of agents
before forwarding to a root aggregator. This scales to large
federations while limiting exposure at each level.
The aggregation server (or function, in ring topology) MUST NOT
have access to individual agent updates in plaintext when secure
aggregation is enabled.
## Privacy Mechanisms
### Differential Privacy for Model Updates
Participant agents MUST apply differential privacy to model
updates before transmission. Each update is clipped to a maximum
L2 norm S and perturbed with calibrated Gaussian noise:
- Clipping bound S: limits the influence of any single data point
- Noise scale sigma: calibrated to achieve (epsilon, delta)-
differential privacy for each round
- Composition: total privacy loss across T rounds is tracked using
the moments accountant or Renyi differential privacy
The privacy parameters MUST be declared in the federation
configuration and enforced by each participant agent.
### Secure Aggregation Protocol
The aggregation server MUST implement a secure aggregation protocol
such that:
1. Each participant agent secret-shares its update using pairwise
keys established with other participants.
2. The aggregation server collects masked updates from all
participants.
3. After a configurable threshold of participants have submitted
updates, the server reconstructs only the aggregate sum.
4. Individual updates are never available to the server in
plaintext.
Dropped participants are handled by reconstructing their masking
contributions from the shares held by surviving participants.
### Privacy Budget Tracking and Enforcement
Each federation MUST maintain a privacy budget tracker that records
cumulative epsilon expenditure per participant. The tracker MUST:
- Record the epsilon cost of each federated learning round
- Refuse to include a participant whose cumulative epsilon would
exceed the configured maximum budget
- Support budget refresh after a configurable cooldown period
- Report remaining budget to participants upon request
Privacy budget state MUST be recorded in ECTs (see {{ect-integration}})
to provide a cryptographic audit trail of privacy expenditure.
### Gradient Compression with Privacy Preservation
To reduce communication overhead, participants MAY compress model
updates using techniques such as top-k sparsification or random
sparsification. Compression MUST NOT reduce the effective privacy
guarantee below the declared epsilon -- noise MUST be added after
compression, calibrated to the compressed update's sensitivity.
## Data Leakage Prevention
### Membership Inference Attack Mitigation
Federation participants MUST apply differential privacy at
sufficient epsilon levels to bound the success rate of membership
inference attacks. The aggregation server SHOULD monitor update
distributions for anomalous patterns indicative of membership
inference attempts.
### Model Inversion Attack Prevention
To prevent reconstruction of training data from model updates:
- Updates MUST be clipped and noised per the differential privacy
mechanism defined above.
- The aggregation server MUST NOT release per-participant update
statistics.
- Participants SHOULD limit the number of rounds in which they
participate with unchanged local data.
### Update Poisoning Detection
The aggregation server MUST implement poisoning detection to
identify malicious updates that attempt to corrupt the global
model:
- Statistical outlier detection on update norms and directions
- Byzantine-robust aggregation (e.g., coordinate-wise median or
trimmed mean) as an alternative to simple averaging
- Participants submitting suspected poisoned updates SHOULD be
flagged and excluded from subsequent rounds pending review
### Privacy Attestation via ECT
Each federated learning round MUST produce an ECT
{{I-D.nennemann-wimse-ect}} attesting to the privacy mechanisms
applied. The ECT `ext` claim MUST include:
~~~json
{
"ext": {
"fed.round_id": "round-42",
"fed.epsilon": 1.5,
"fed.delta": 1e-5,
"fed.participants": 12,
"fed.aggregation": "secure_aggregation",
"fed.poisoning_detected": false
}
}
~~~
{: #fig-privacy-attestation title="Privacy Attestation in ECT Extension Claims"}
## Privacy Policy Format
Federation participants MUST publish a machine-readable privacy
policy document describing their federation parameters. The policy
is a JSON object:
~~~json
{
"federation_policy_version": "1.0",
"max_epsilon_per_round": 2.0,
"max_total_epsilon": 10.0,
"delta": 1e-5,
"secure_aggregation_required": true,
"min_participants": 3,
"budget_refresh_seconds": 86400,
"allowed_topologies": ["star", "hierarchical"],
"data_categories_excluded": ["PII", "PHI"]
}
~~~
{: #fig-privacy-policy title="Machine-Readable Privacy Policy"}
Privacy level claims SHOULD be included in the ECT `ext` field
as `fed.policy_hash`, containing the SHA-256 hash of the
federation privacy policy document, enabling verifiers to confirm
that a specific policy was in effect during a learning round.
# Cross-Protocol Agent Migration
## Migration Model
~~~
+-----------------------------------------------------------+
| Migration Flow |
| |
| Source Domain (Protocol A) Dest Domain (Protocol B) |
| +---------------------+ +---------------------+ |
| | | | | |
| | [Agent] | | [Agent] | |
| | | | | ^ | |
| | | 1.trigger | | | | |
| | v | | 5.resume | |
| | [Serialize State] | | | | |
| | | | | [Deserialize State]| |
| | | 2.package | | ^ | | |
| | v | | |4.recv| | |
| | [Migration Msg]----|--3.transfer--|------+ | |
| | | | | |
| +---------------------+ +---------------------+ |
| |
| ECT Chain: migration_start -> migration_transfer |
| -> migration_complete |
+-----------------------------------------------------------+
~~~
{: #fig-migration-model title="Agent Migration Between Domains"}
## Migration Protocol
### Migration Trigger Events and Conditions
A migration MAY be triggered by any of the following events:
- Operator-initiated domain transfer
- Load balancing across infrastructure providers
- Disaster recovery failover
- Protocol deprecation requiring protocol change
- Policy-driven relocation (e.g., data sovereignty requirements)
The migration trigger MUST be recorded in an ECT with
`exec_act` set to `"migration_start"`.
### Pre-Migration Capability Check
Before initiating migration, the source environment MUST verify
that the destination environment supports the agent's required
capabilities:
1. Query the destination's capability advertisement endpoint.
2. Verify that all required agent capabilities can be mapped to
the destination protocol.
3. Verify that the destination accepts the agent's identity
format (e.g., SPIFFE ID).
4. Confirm sufficient resources at the destination for the
agent's state size.
If any check fails, the migration MUST be aborted and an error
reported to the triggering entity.
### State Serialization Format
Agent state MUST be serialized using CBOR (Concise Binary Object
Representation) with the following top-level structure:
~~~
migration_state = {
"version": uint, ; serialization format version
"agent_id": tstr, ; agent SPIFFE ID
"source_protocol": tstr, ; source protocol identifier
"dest_protocol": tstr, ; destination protocol identifier
"timestamp": uint, ; Unix timestamp of serialization
"state": {
"context": bstr, ; conversation/task context
"memory": bstr, ; long-term memory store
"learned_params": bstr, ; model parameters or embeddings
"active_tasks": [* task] ; in-progress task descriptors
},
"ect_chain": [* tstr], ; ECT JWS chain for identity
"integrity": tstr ; HMAC-SHA256 of state fields
}
~~~
{: #fig-state-format title="CBOR Migration State Structure"}
### Identity Transfer and Re-Attestation
During migration, the agent's identity MUST be preserved through
the ECT chain:
1. The source environment issues a migration ECT with the full
ECT chain as the `par` claim.
2. The destination environment verifies the ECT chain back to a
trusted root.
3. The destination environment issues a new ECT for the agent with
`exec_act` set to `"migration_complete"` and `par` referencing
the migration transfer ECT.
4. The agent's SPIFFE ID remains unchanged; only the issuing
authority for new ECTs changes.
### Post-Migration Verification
After migration completes, the destination environment MUST:
1. Verify state integrity using the HMAC in the migration payload.
2. Deserialize and load the agent state.
3. Execute a capability self-test to confirm operational readiness.
4. Issue the `"migration_complete"` ECT.
5. Notify the source environment of successful migration so it
can release resources.
If verification fails, the destination MUST notify the source
environment, which SHOULD retain the agent in its original state
for retry or rollback.
## State Transfer
### Agent State Components
An agent's transferable state consists of four components:
Context:
: The current conversation or task execution context, including
recent message history and active reasoning chains.
Memory:
: Long-term memory stores such as retrieval-augmented generation
(RAG) indices, episodic memory, or key-value caches.
Learned Parameters:
: Fine-tuned model weights, adapter layers, embeddings, or
reinforcement learning policies specific to the agent's role.
Active Tasks:
: In-progress task descriptors including task ID, current step,
dependencies, and expected outputs.
### Incremental State Transfer for Large State
For agents with state exceeding 10 MB, incremental transfer
MUST be supported:
1. The source environment transmits a state manifest listing all
state chunks with their SHA-256 hashes.
2. The destination environment requests only chunks it does not
already possess (delta transfer).
3. Each chunk transfer is individually acknowledged.
4. After all chunks are received, the destination assembles the
complete state and verifies the root hash.
### State Integrity Verification
State integrity MUST be verified at each stage:
- Before transmission: source computes HMAC-SHA256 over the
serialized state using a key derived from the migration ECT.
- During transmission: TLS provides transport integrity.
- After reception: destination recomputes and verifies the HMAC.
- After deserialization: destination runs a state consistency
check (e.g., verifying that active task references resolve).
## Protocol Bridges
### Bridge Architecture for Common Protocols
Protocol bridges translate agent communication between protocols
while preserving semantic equivalence. A bridge MUST support
bidirectional translation for each protocol pair it advertises.
~~~
[Agent (A2A)] <--A2A--> [Bridge] <--MCP--> [Agent (MCP)]
|
[ECT Logger]
~~~
{: #fig-bridge-arch title="Protocol Bridge Architecture"}
Each bridge instance MUST:
- Maintain a mapping table for message types between protocols.
- Preserve task identifiers across protocol boundaries.
- Record each translation as an ECT with `exec_act` set to
`"bridge_translate"`.
### Context Translation Rules
When translating context between protocols, bridges MUST:
- Map equivalent fields (e.g., A2A "task" to MCP "resource").
- Preserve all metadata as extension fields where direct mapping
is not available.
- Flag semantic mismatches in the translation ECT's `ext` claim
under `bridge.warnings`.
### Capability Re-Mapping
Agent capabilities expressed in the source protocol MUST be
re-mapped to the closest equivalent in the destination protocol.
Capabilities with no equivalent MUST be listed in the migration
state as `unmapped_capabilities` so the destination environment
can handle them appropriately (e.g., by loading additional
tooling or reporting reduced functionality).
## Privacy During Migration
### Context Sanitization Before Transfer
Before state serialization, the source environment MUST sanitize
the agent's context by:
- Removing credentials, API keys, and session tokens.
- Redacting PII unless the destination is authorized to receive
it per the agent's privacy policy.
- Stripping environment-specific configuration (e.g., internal
hostnames, file paths).
### Selective State Disclosure
The migration protocol supports selective state disclosure:
the source environment MAY omit state components that the
destination is not authorized to receive. The migration state
manifest indicates which components are included and which are
withheld, allowing the destination to request missing components
through an authorized channel if needed.
### No-Context-Leakage Guarantees to New Host
The destination environment MUST NOT have access to state
components that were excluded during selective disclosure. The
migration protocol provides the following guarantees:
- State components are individually encrypted with component-
specific keys.
- Only authorized components have their keys transmitted to the
destination.
- The destination cannot derive keys for withheld components
from the keys it receives.
- The migration ECT records which components were transferred,
enabling audit of information flow.
# ECT Integration {#ect-integration}
## Privacy Attestation Claims
ECTs produced during federated learning rounds MUST include
privacy attestation claims in the `ext` field as defined in
{{privacy-attestation-via-ect}}. These claims enable any
verifier in the ECT chain to confirm that appropriate privacy
mechanisms were applied without accessing the underlying data.
## Migration Evidence Chain
Migration events produce a chain of three ECTs that together
provide a complete cryptographic record of the migration:
~~~
ECT 1: exec_act = "migration_start"
- Records: trigger reason, source domain, agent ID
- par: references the agent's most recent operational ECT
ECT 2: exec_act = "migration_transfer"
- Records: state hash, components transferred, dest domain
- par: references ECT 1
- inp_hash: SHA-256 of serialized migration state
ECT 3: exec_act = "migration_complete"
- Records: verification result, new domain, resumed capabilities
- par: references ECT 2
- Issued by: destination environment
~~~
{: #fig-migration-ect-chain title="Migration ECT Evidence Chain"}
This three-ECT chain ensures that migration events are
non-repudiable and auditable. Any party with access to the
ECT chain can verify that a migration occurred, what state was
transferred, and whether it completed successfully.
## Federation Participation Records
Each agent's participation in federated learning MUST be
recorded in the ECT DAG. The aggregation server issues a
per-round ECT with `exec_act` set to `"fed_aggregate"` and
`par` referencing the ECTs of all participating agents for
that round. This creates a verifiable record of federation
participation without revealing the content of individual
updates.
# Security Considerations
## Privacy Budget Exhaustion Attacks
An attacker controlling the aggregation server or a quorum of
participants could attempt to exhaust a victim participant's
privacy budget by triggering excessive learning rounds.
Mitigations include:
- Participant-side rate limiting on round participation.
- Privacy budget enforcement at the participant, not solely
at the aggregation server.
- ECT-based audit trails enabling detection of abnormal round
frequency.
## Migration Hijacking
An attacker could attempt to redirect a migration to a
malicious destination. Mitigations include:
- Mutual TLS authentication between source and destination.
- Destination identity verification via SPIFFE ID in the
migration ECT.
- Operator confirmation for migrations to previously unknown
destinations.
## State Tampering During Transfer
An attacker with access to the network path could attempt to
modify the migration state in transit. Mitigations include:
- HMAC-SHA256 integrity protection of the serialized state.
- TLS 1.3 for transport security.
- Post-migration state verification at the destination.
- ECT `inp_hash` recording the expected state hash.
## Protocol Bridge Vulnerabilities
Protocol bridges are trusted intermediaries that could be
compromised to:
- Modify messages during translation.
- Exfiltrate sensitive data from translated messages.
- Inject malicious content into translated messages.
Mitigations include:
- ECT audit trails for all bridge translations.
- Input/output hash verification via `inp_hash`/`out_hash`.
- Bridge attestation using hardware security modules where
available.
## Federation Participant Compromise
A compromised participant could attempt to:
- Submit poisoned updates to corrupt the global model.
- Conduct inference attacks on other participants' updates
observed during ring topology forwarding.
- Collude with the aggregation server to bypass secure
aggregation.
Mitigations include:
- Byzantine-robust aggregation algorithms.
- Secure aggregation preventing server access to individual
updates.
- Anomaly detection on update distributions.
- ECT-based participation records enabling forensic analysis.
# IANA Considerations
This document requests the following IANA registrations:
## ECT Action Type Registry
Registration of the following `exec_act` values in a future ECT
action type registry:
| Value | Description |
|:---------------------|:-------------------------------------|
| migration_start | Agent migration initiated |
| migration_transfer | Agent state transferred |
| migration_complete | Agent migration completed |
| fed_aggregate | Federated learning round aggregated |
| bridge_translate | Protocol bridge translation |
## ECT Extension Claims Registry
Registration of the following `ext` claim prefixes:
| Prefix | Description |
|:---------|:-----------------------------------------|
| fed. | Federated learning privacy claims |
| mig. | Migration-related claims |
| bridge. | Protocol bridge claims |
## Media Type Registration
Registration of the following media type:
- Type name: application
- Subtype name: agent-migration-state+cbor
- Required parameters: none
- Optional parameters: version
- Encoding: binary (CBOR)
- Purpose: Serialized agent migration state for cross-protocol
agent migration as defined in this document.
--- back
# Acknowledgments
{:numbered="false"}
This document builds on the Execution Context Token specification
{{I-D.nennemann-wimse-ect}} and the Agent Context Policy Token
{{I-D.nennemann-agent-dag-hitl-safety}}. The gap analysis
{{I-D.nennemann-agent-gap-analysis}} identified the requirements
addressed by this document.