Governance-Aware Federated Learning for Multi-Agent AI Systems: A Policy-Driven Framework for Trustworthy Collaborative AI

Authors: Kiranmayee Janardhan , Karthick S , Madhusmita Panda , Bharathi Vokkaliga Ganesh , Syed Ahmed

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Abstract: Federated Learning (FL) enables collaborative model training without exchanging raw data, but conventional FL provides limited runtime governance, evidence verification, fairness assessment, explainability monitoring, and auditability. This paper proposes Governance-Aware Federated Learning (GAFL), which combines independent governance-evidence verification, temporal trust, fairness and explanation assessment, anomaly detection, policy enforcement, normalized aggregation, and tamper-evident audit logging. Client-reported governance values are authenticated, bound to their submitted model updates, and independently recomputed before aggregation. A cross-silo prototype with 10 participants was evaluated on the Adult Income dataset under honest, benign non-IID, biased-data, malicious-update, non-compliant, falsified-evidence, and mixed-threat scenarios. Four methods were compared across five random seeds: FedAvg, FedAvg with anomaly filtering, GAFL without independent verification, and complete GAFL. Complete GAFL intervened on all affected participants at the final round across the evaluated risk scenarios, including identifying 100% of falsified-evidence participants, whereas GAFL without verification identified none of the falsified-evidence participants. Final macro-F1 ranged from 0.7520 to 0.7733 across GAFL scenarios. Complete GAFL required 173.67 plus or minus 26.94 ms of governance processing per round, compared with 3.82 plus or minus 0.79 ms for FedAvg. A strict demographic-parity threshold also caused frequent interventions for honest participants, demonstrating a significant fairness-policy and utility trade-off.

Comments:

16 pages, 2 Figures and 3 Tables presented at NGEN 2026 Conference

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.38411 [cs.AI]

(or

arXiv:2610.38411v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.38411

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kiranmayee Janardhan Dr [ view email ]

[v1]

Wed, 28 Oct 2026 07:57:10 UTC (610 KB)

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