FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
Federated learning for generalization, robustness, fairness: A survey and benchmark
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Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.
citing papers explorer
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From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
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Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
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Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.