Poisoning Attacks and Defenses in Federated Learning: A Survey
Subhash Sagar, Chang-Sun Li, Seng W. Loke, Jinho Choi · arXiv (Cornell University) · 2023
Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.