Trust‑Aware Federated Aggregation in Multi‑Agent Settings

TITLE OF THE INVENTION

Trust‑Aware Federated Aggregation Architecture for Multi‑Agent Systems

FIELD OF THE INVENTION

The present invention relates to distributed machine learning, specifically to federated learning systems that incorporate dynamic trust assessment, adaptive differential privacy, blockchain‑based auditability, and quantum‑resilient aggregation for heterogeneous multi‑agent networks such as UAV fleets, IoT edge nodes, autonomous vehicles, and industrial cyber‑physical systems.

BACKGROUND AND PRIOR ART

Federated learning (FL) enables collaborative model training without sharing raw data, yet remains vulnerable to data‑poisoning, Byzantine, and targeted adversarial updates. Conventional defenses such as simple averaging or static robust statistics (e.g., trimmed mean) are insufficient against coordinated attacks and adaptive poisoning ^[v7136], ^[v16338]. Recent work introduces reputation‑based client selection and adaptive weighting to mitigate malicious influence ^[v15154], ^[v12128]. However, these approaches lack end‑to‑end privacy enforcement, verifiable aggregation, and transparent audit trails required by emerging regulations such as the EU AI Act and ISO/IEC 42001. Adaptive differential privacy (DP) schemes that scale noise by client reputation have been proposed, yet they do not provide verifiable compliance proofs ^[v12800], ^[v12837]. Blockchain‑enabled trust ledgers offer tamper‑resistant audit trails, but prior art does not integrate them with reputation engines, adaptive DP, and quantum‑resilient aggregation ^[v9402], ^[v13219]. Consequently, there remains a technical problem of providing a unified, trust‑aware federated aggregation framework that simultaneously guarantees robustness, privacy, auditability, and scalability in adversarial, resource‑constrained multi‑agent settings.

SUMMARY OF THE INVENTION

The invention discloses a Trust‑Adaptive Federated Aggregation (TAFA) architecture that unifies a Multi‑Dimensional Reputation Engine (MDRE), an Adaptive Differential Privacy Layer (ADPL), a Blockchain‑Enabled Trust Ledger (BLTL), a Quantum‑Resilient Aggregation Core (QRAC), a Federated Graph Contrastive Learning Module (FGCLM), and a Zero‑Shot Policy Transfer module (ZSTTM). MDRE computes a continuous reputation vector from statistical consistency, temporal behavior, content similarity, and cryptographic attestations, and applies Bayesian thresholding for soft exclusion. ADPL scales DP noise inversely with reputation and emits zero‑knowledge proofs of compliance. BLTL records reputation scores, update hashes, and ZKP commitments on a lightweight smart‑contract chain, providing immutable auditability and token‑based governance. QRAC employs Grover‑style amplitude amplification and entanglement checks to prioritize trustworthy updates and thwart quantum‑adversaries. FGCLM aggregates contrastive loss vectors weighted by reputation, reducing communication overhead and mitigating malicious graph structures. ZSTTM aggregates policies using a Bayesian trust metric while balancing explainability and performance. The integrated pipeline delivers robust, privacy‑preserving, auditable, and scalable federated learning for heterogeneous multi‑agent networks.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiment 1 – Multi‑Dimensional Reputation Engine (MDRE)
MDRE computes a reputation vector R = (R_stat, R_temp, R_sim, R_crypto) for each client. R_stat derives from gradient norms and loss variance; R_temp is an exponential moving average (EMA) of per‑round quality; R_sim is the cosine similarity between the local update and the current global model; R_crypto verifies signed update signatures. Bayesian inference updates each dimension’s posterior probability, and a dynamic threshold τ is recalculated using a Bayesian update rule that incorporates recent convergence speed and detected attack intensity ^[12][15]. Soft exclusion weights each update by a continuous reputation score, enabling graceful degradation and re‑inclusion of previously penalized clients ^[11].

Embodiment 2 – Adaptive Differential Privacy Layer (ADPL)
ADPL modulates the DP noise scale σ by the reputation score: σ = σ_max · (1 – R_avg), where R_avg is the mean of the reputation dimensions. High‑trust clients receive lower noise, improving utility, while low‑trust clients receive stronger protection ^[16]. Each client generates a zero‑knowledge proof (ZKP) that the applied noise satisfies the privacy budget without revealing the budget itself ^[13]. The ZKP is a recursive proof that can be verified on the blockchain ledger.

Embodiment 3 – Blockchain‑Enabled Trust Ledger (BLTL)
BLTL records, in a lightweight smart‑contract chain, the reputation vector, the hash of each client update, and the ZKP commitment. The ledger is immutable and tamper‑resistant, providing an external audit point for regulators ^[13]. Clients stake governance tokens proportional to their historical reputation; malicious behavior drains the stake, providing an economic deterrent ^[17].

Embodiment 4 – Quantum‑Resilient Aggregation Core (QRAC)
QRAC applies a Grover‑style amplitude amplification operator to each client’s update vector, prioritizing updates with higher inner‑product similarity to the global model. The weighting factor w_i = 1 + α·cos(θ_i), where θ_i is the angle between client i’s update and the global model, and α is a tunable amplification parameter ^[10]. For quantum‑capable nodes, entangled qubits are used to jointly verify that all participants observe the same global state; a sudden drop in purity triggers a rollback ^[18].

Embodiment 5 – Federated Graph Contrastive Learning Module (FGCLM)
Clients construct local graph embeddings of multimodal data (video, temperature, network traffic) and compute a contrastive loss vector L_i. Only L_i and a prototype vector are transmitted, reducing payload. Aggregation weights L_i by the reputation score, mitigating over‑fitting to malicious graph structures ^[19][20].

Embodiment 6 – Zero‑Shot Policy Transfer with Trust Metrics (ZSTTM)
In multi‑agent reinforcement learning, each agent’s policy update is weighted by a Bayesian trust metric τ_i derived from MDRE. An explainability controller allocates a budget between fidelity of explanations and policy performance, ensuring regulatory compliance without sacrificing effectiveness ^[21].

Overall Pipeline
Clients train locally, compute reputation features, apply context‑aware DP, generate ZKPs, and submit updates to the aggregation core. The core aggregates, updates reputation, records proofs on the blockchain, and disseminates the new global model. The system is communication‑efficient (sparsification, prototype sharing), scalable (sharded ledger), and resilient to classical and quantum adversaries.

CLAIMS

1. A method for trust‑aware federated aggregation in a multi‑agent network, comprising: receiving local model updates from a plurality of agents; computing a multi‑dimensional reputation vector for each agent based on statistical consistency, temporal behavior, content similarity, and cryptographic attestations; applying a Bayesian threshold to determine a continuous reputation score; scaling differential privacy noise inversely proportional to the reputation score; generating a zero‑knowledge proof that the noise scale complies with the privacy budget; recording the reputation score, update hash, and proof on a blockchain ledger; aggregating the noise‑scaled updates weighted by the reputation scores to produce a global model; updating the reputation vectors based on the aggregated result; and disseminating the updated global model to the agents. The method further includes quantum‑inspired weighting of updates and entanglement consistency checks ^[12][10][13].

2. A system for trust‑aware federated aggregation, comprising: a client interface for local training and update submission; a reputation engine that computes a multi‑dimensional reputation vector; a differential privacy module that scales noise based on reputation; a zero‑knowledge proof generator; a blockchain ledger for recording reputation scores, update hashes, and proofs; an aggregation core that applies quantum‑inspired weighting and entanglement checks; and a global model distributor. The system further includes a governance token staking mechanism and a federated graph contrastive learning module ^{[12][10][13][17]}.

3. The method of claim 1, wherein the reputation vector includes a cosine similarity component between the local update and the current global model ^[19].

4. The method of claim 1, wherein the Bayesian threshold is updated using a Bayesian update rule that incorporates recent convergence speed and detected attack intensity ^[12][15].

5. The method of claim 1, wherein the differential privacy noise scale is modulated by the reputation score such that higher reputation yields lower noise ^[16].

6. The method of claim 1, wherein the zero‑knowledge proof is a recursive ZKP that proves compliance with the noise budget without revealing the budget itself ^[13].

7. The method of claim 1, wherein the blockchain ledger is a lightweight smart‑contract chain that records reputation scores, update hashes, and ZKP commitments ^[13].

8. The method of claim 1, further comprising a quantum‑inspired weighting scheme based on Grover amplitude amplification to prioritize updates with higher inner‑product similarity to the global model ^[10].

9. The method of claim 1, further comprising an entanglement‑based consistency check for quantum‑capable nodes ^[18].

10. The method of claim 1, wherein the aggregation step includes a soft exclusion mechanism that weights updates by a continuous reputation score rather than hard dropping ^[11].

11. The method of claim 1, wherein the aggregation includes a federated graph contrastive learning module that aggregates contrastive loss vectors weighted by reputation scores ^[19][20].

12. The method of claim 1, wherein the aggregation includes a zero‑shot policy transfer module that aggregates policies using a Bayesian trust metric ^[21].

13. The method of claim 1, wherein the system includes a governance token that clients stake proportional to historical reputation, and malicious behavior drains the stake ^[17].

14. The method of claim 1, wherein the system is deployed in a heterogeneous multi‑agent network comprising UAVs, IoT nodes, autonomous vehicles, and industrial cyber‑physical systems ^[12].

15. The method of claim 1, wherein the system achieves communication efficiency by transmitting only contrastive loss vectors and prototype embeddings ^[19][20].

16. The method of claim 1, wherein the system provides interpretability by exposing the reputation vector and the rationale for weighting to human operators ^[13][21].

17. The method of claim 1, wherein the system includes a zero‑knowledge proof audit trail that can be inspected by regulators ^{[v14162][v5668]}.

ABSTRACT

A trust‑aware federated aggregation architecture (TAFA) for heterogeneous multi‑agent networks integrates a multi‑dimensional reputation engine, adaptive differential privacy, blockchain‑based auditability, quantum‑resilient weighting, federated graph contrastive learning, and zero‑shot policy transfer. Clients compute reputation features, apply reputation‑scaled DP noise, generate zero‑knowledge proofs, and submit updates to a blockchain ledger. The aggregation core applies Bayesian thresholding, soft exclusion, Grover‑style amplitude amplification, and entanglement checks to produce a robust global model. The system achieves high resilience to poisoning and Byzantine attacks, preserves privacy with verifiable DP, provides immutable audit trails, and maintains communication efficiency through prototype sharing. TAFA satisfies emerging regulatory requirements for interpretability and auditability while enabling scalable, secure, and trustworthy collaborative AI across UAV fleets, IoT nodes, autonomous vehicles, and industrial cyber‑physical systems.