Federated Learning Framework via Distributed Mutual Learning

Yash Gupta

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arXiv · 2503.05803

Federated Learning Framework via Distributed Mutual Learning

Yash Gupta

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Abstract

Federated Learning often relies on sharing full or partial model weights, which can burden network bandwidth and raise privacy risks. We present a loss-based alternative using distributed mutual learning. Instead of transmitting weights, clients periodically share their loss predictions on a public test set. Each client then refines its model by combining its local loss with the average Kullback-Leibler divergence over losses from other clients. This collaborative approach both reduces transmission overhead and preserves data privacy. Experiments on a face mask detection task demonstrate that our method outperforms weight-sharing baselines, achieving higher accuracy on unseen data while providing stronger generalization and privacy benefits.

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Yash Gupta. 2025-03-03. Federated Learning Framework via Distributed Mutual Learning. https://arxiv.org/abs/2503.05803

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