Learning to Backdoor Federated Learning. (arXiv:2303.03320v2 [cs.LG] UPDATED)

In a federated learning (FL) system, malicious participants can easily embed
backdoors into the aggregated model while maintaining the model's performance
on the main task. To this end, various defenses, including training stage
aggregation-based defenses and post-training mitigation defenses, have been
proposed recently. While these defenses obtain reasonable performance against
existing backdoor attacks, which are mainly heuristics based, we show that they
are insufficient in the face of more advanced attacks. In particular, we
propose a general reinforcement learning-based backdoor …

advanced aggregation arxiv attacks backdoor embed face federated learning framework general heuristics performance policy reinforcement reinforcement learning stage training trains