FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models

arXiv:2403.02846v1 Announce Type: cross
Abstract: Federated Learning (FL) thrives in training a global model with numerous clients by only sharing the parameters of their local models trained with their private training datasets. Therefore, without revealing the private dataset, the clients can obtain a deep learning (DL) model with high performance. However, recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model when adversaries, posed as benign clients, are present in a group of …

arxiv cs.ai cs.cr cs.dc cs.lg federated federated learning