FedVSSAM mitigates flatness incompatibility in SAM-based federated learning by consistently using a variance-suppressed adjusted direction for local perturbation, descent, and global updates, with non-convex convergence guarantees.
FedCM: Federated learning with client-level momentum
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FedInit uses reverse personalized initialization in FL to reduce client drift effects, showing via excess risk that inconsistency impacts generalization error more than optimization error.
citing papers explorer
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FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
FedVSSAM mitigates flatness incompatibility in SAM-based federated learning by consistently using a variance-suppressed adjusted direction for local perturbation, descent, and global updates, with non-convex convergence guarantees.
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Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
FedInit uses reverse personalized initialization in FL to reduce client drift effects, showing via excess risk that inconsistency impacts generalization error more than optimization error.