AR2FL: Anomaly-Resistant Robust Framework for Federated Learning

Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving privacy. However, FL systems are vulnerable to attacks from malicious clients that can degrade model performance and compromise integrity. In this work, we propose AR2FL, an anomaly-resistant and robust framework that enhances FL aggregation by leveraging mean latent representations of client updates. This data-driven approach enables the server to estimate inter-client similarity and dynamically scale clients contributions, reducing the influence of anomalous or adversarial updates. Unlike methods based on fixed distance metrics such as cosine similarity or Euclidean distance, AR2FL captures deeper statistical patterns in the latent space, enabling more accurate and secure model updates. Experiments on several datasets show AR2FL maintains strong accuracy, fast convergence, and high robustness, making it suitable for secure large-scale FL.