Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions

Recent advancements in federated learning (FL) have produced models that retain user privacy by training across multiple decentralized devices or systems holding local data samples. However, these strategies often neglect the inherent challenges of statistical heterogeneity and vulnerability to adversarial attacks, which can degrade model robustness and fairness. Personalized FL strategies offer some respite by adjusting models to fit individual client profiles, yet they tend to neglect server-side aggregation vulnerabilities. To address these issues, we propose Reinforcement Federated Learning (RFL), …

adversarial adversarial attacks attacks catalyst challenges client cs.ai cs.dc cs.lg data decentralized devices dynamics fair federated learning multiple privacy reinforcement reinforcement learning robust samples statistical strategies systems training vulnerability