Non-IID

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

This work proposes a backdoor defense for FL from individual perspectives, which excludes infected model updates while maintaining main task accuracy.

Feb 28, 2024
FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity
FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity

This work proposes a personalized FL approach for cross-silo scenarios, which achieves adaptability of the statistical heterogeneity among client-side data.

Aug 7, 2023