  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.
