A new framework called Governance-Aware Federated Learning (GAFL) has been proposed to enhance trust and security in collaborative AI systems. This framework integrates independent verification of governance values, temporal trust mechanisms, fairness and explanation assessments, and tamper-evident audit logging. Experiments on the Adult Income dataset demonstrated that GAFL effectively identifies malicious participants and falsified evidence, though it also revealed a trade-off between fairness policies and utility, leading to frequent interventions for honest participants under strict demographic-parity thresholds. The additional governance processing per round for GAFL was significantly higher than standard FedAvg. AI
IMPACT This framework could improve the security and trustworthiness of collaborative AI model development, particularly in sensitive domains.
RANK_REASON The cluster contains an academic paper detailing a new framework for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]
- Adult Income dataset
- FedAvg
- Federated Learning
- Governance-Aware Federated Learning
- Kiranmayee Janardhan
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