Model Stability Defense Against Model Poisoning in Federated Learning

Summary

This research paper proposes Model Stability Defense for Federated Learning (MSDFL), a method that defends federated learning systems against model poisoning by minimizing the norm of the model-output Jacobian matrix. A refined variant, HMSDFL, extends stability to all logit output dimensions to address convergence-speed disparities caused by MSDFL. The authors report extensive experiments showing fidelity, robustness, compatibility, and self-protection, though the source text gives no numerical results.

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