Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis

In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our approach uniquely combines detecting anomalous gradient patterns with identifying reconstruction errors, significantly enhancing FL model security. Validated through extensive experiments on MNIST and CIFAR-10 datasets, our method outperforms existing solutions by 15\% in anomaly detection accuracy while maintaining a minimal false positive rate. This robust performance, consistent across varied data types and network sizes, underscores our framework's potential in securing FL deployments in critical domains such as healthcare and finance. By setting new benchmarks for anomaly detection within FL, our work paves the way for future advancements in distributed learning security.