FORCE: Byzantine-Resilient Decentralized Federated Learning via Game-Theoretic Contribution Aggregation

Summary

The paper proposes FORCE, a Byzantine-resilient aggregation scheme for Decentralized Federated Learning (DFL), where clients share models directly without a central server. Existing defenses rely on gradient checks that adaptive adversaries can manipulate, so FORCE instead uses the loss of each local model, inspired by the Shapley value from game theory, to flag potentially malicious clients. A lightweight variant, FORCE⁻, approximates the Shapley computation to reduce overhead as neighbor counts grow, and experiments on four datasets under three attacks report that FORCE outperforms existing state-of-the-art methods.

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