A review on client-server attacks and defenses in federated learning

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

“…Some developments, such as federated distillation, moving target defense, trusted execution environments, DP-powered FL for parameter/update security, anomaly detection, pruning, ZKPs, adversarial training, legitimate client recognition, clipping gradients, federated multi-task learning, and SMC, have recently emerged to tackle these PAs and their variants. Although these approaches are handy, certain limitations exist regarding emerging adversarial threats [69]. Hence, proposing practical methods to enhance security and privacy in the FL ecosystem from major adversarial attacks without compromising accuracy is an interesting area of research.…”