AMF-CFL: Anomaly model filtering based on clustering in federated learning

Federated learning (a system where multiple participants train a shared AI model without sharing their raw data) is vulnerable to attacks from malicious clients who send harmful model updates. This paper proposes AMF-CFL, a defense method that uses multi-k means clustering (a technique for grouping similar data points) and z-score statistical analysis (a way to identify unusual values) to filter out malicious updates and protect the global model, even when clients have non-i.i.d. data distributions (when each participant's data differs significantly in type and quantity).