  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 clients. Therefore, recent studies suggested
byzantine-robust FL methods that allow the server to train an accurate global
model even with the adversaries present in the system. However, many existing
methods require the knowledge of the number of malicious clients or the
auxiliary (clean) dataset or the effectiveness reportedly decreased hugely when
the private dataset was non-independently and identically distributed
(non-IID). In this work, we propose FLGuard, a novel byzantine-robust FL method
that detects malicious clients and discards malicious local updates by
utilizing the contrastive learning technique, which showed a tremendous
improvement as a self-supervised learning method. With contrastive models, we
design FLGuard as an ensemble scheme to maximize the defensive capability. We
evaluate FLGuard extensively under various poisoning attacks and compare the
accuracy of the global model with existing byzantine-robust FL methods. FLGuard
outperforms the state-of-the-art defense methods in most cases and shows
drastic improvement, especially in non-IID settings.
https://github.com/201younghanlee/FLGuard
