BART-FL is a lightweight defense framework for secure federated learning that detects and filters malicious client updates before aggregation. By integrating PCA, cosine similarity, K-means clustering, and a multi-metric statistical voting mechanism, BART-FL effectively defends against backdoor attacks while preserving model accuracy and computational efficiency across multiple benchmark datasets.
