FEDERATED LEARNING FOR PRIVACY AWARE DISTRIBUTED ARTIFICIAL INTELLIGENCE

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Abstract

Federated learning enables multiple participants to train a shared machine learning model while retaining training records within their local environments. Its practical value depends on how distributed optimization interacts with information leakage, unequal data distributions, communication costs, and institutional governance. This narrative review examines foundational work on federated averaging, heterogeneous optimization, secure aggregation, differential privacy, and gradient leakage. It develops a conceptual deployment and evaluation framework for collaboration among educational institutions. The analysis distinguishes local data retention from formal privacy protection and separates predictive performance from equitable performance across participating sites. The proposed framework combines a documented threat model, controlled participation, privacy accounting, and evaluation against local and centralized baselines where feasible. No new dataset, implementation, or experimental results are presented. The paper concludes that deployment decisions should depend on measured benefit over local models, explicit privacy assumptions, and acceptable costs for the least resourced participant. Astaged pilot is proposed to test these conditions before operational adoption.

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