LNTrust has nodes learn compact trust functions from validation evidence that both guide training distillation and define deployment ensembles, yielding higher accuracy with less communication than prior output-only baselines.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
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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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Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning
LNTrust has nodes learn compact trust functions from validation evidence that both guide training distillation and define deployment ensembles, yielding higher accuracy with less communication than prior output-only baselines.
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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.