2 papers
cs.LG2024
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung +2
Federated learning is a promising framework to train neural networks with widely distributed data. However, performance degrades heavily with heterogeneously distributed data. Rece…
cs.CR2022
FedCC: Robust Federated Learning against Model Poisoning Attacks
Hyejun Jeong, Hamin Son, Seohu Lee +2
Federated learning is a distributed framework designed to address privacy concerns. However, it introduces new attack surfaces, which are especially prone when data is non-Independ…