Search Results

Federated learning (FL) encounters significant challenges in heterogeneous client environments, due to statistical heterogeneity affecting the global model’s generalization. To address these issues, we introduce FedGAC, an efficient adaptive ...

Most existing model poisoning attacks in federated learning (FL) control a set of malicious clients and share a fixed number of malicious gradients with the server in each FL training round, to achieve a desired tradeoff between the attack impact and ...