Federated learning allows multiple clients to collaboratively train models while preserving privacy, but it is vulnerable to poisoned gradients from malicious participants. Poison-resistant gradient aggregation techniques, such as trimmed mean, median, clustering, and norm-based methods, protect model integrity. Adaptive defenses and federated auditing further enhance security against... https://kahkaham.net/read-blog/135910