FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Y ang
–Neural Information Processing Systems
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting
Neural Information Processing Systems
Feb-16-2026, 20:53:20 GMT
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