MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers

Federated Learning (FL) enhances decentralized machine learning by safeguarding data privacy, reducing communication costs, and improving model performance with diverse data sources. However, FL faces vulnerabilities such as untargeted poisoning attacks and targeted backdoor attacks, posing challenges to model integrity and security. Preventing backdoors proves especially challenging due to their stealthy nature. Existing mitigation techniques have shown efficacy but often overlook realistic adversaries and diverse data distributions.