{"ID":2893233,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14322","arxiv_id":"2507.14322","title":"FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning","abstract":"Federated Learning (FL) offers a paradigm for privacy-preserving collaborative AI, but its decentralized nature creates significant vulnerabilities to model poisoning attacks. While numerous static defenses exist, their effectiveness is highly context-dependent, often failing against adaptive adversaries or in heterogeneous data environments. This paper introduces FedStrategist, a novel meta-learning framework that reframes robust aggregation as a real-time, cost-aware control problem. We design a lightweight contextual bandit agent that dynamically selects the optimal aggregation rule from an arsenal of defenses based on real-time diagnostic metrics. Through comprehensive experiments, we demonstrate that no single static rule is universally optimal. We show that our adaptive agent successfully learns superior policies across diverse scenarios, including a ``Krum-favorable\" environment and against a sophisticated \"stealth\" adversary designed to neutralize specific diagnostic signals. Critically, we analyze the paradoxical scenario where a non-robust baseline achieves high but compromised accuracy, and demonstrate that our agent learns a conservative policy to prioritize model integrity. Furthermore, we prove the agent's policy is controllable via a single \"risk tolerance\" parameter, allowing practitioners to explicitly manage the trade-off between performance and security. Our work provides a new, practical, and analyzable approach to creating resilient and intelligent decentralized AI systems.","short_abstract":"Federated Learning (FL) offers a paradigm for privacy-preserving collaborative AI, but its decentralized nature creates significant vulnerabilities to model poisoning attacks. While numerous static defenses exist, their effectiveness is highly context-dependent, often failing against adaptive adversaries or in heteroge...","url_abs":"https://arxiv.org/abs/2507.14322","url_pdf":"https://arxiv.org/pdf/2507.14322v2","authors":"[\"Md Rafid Haque\",\"Abu Raihan Mostofa Kamal\",\"Md. Azam Hossain\"]","published":"2025-07-18T18:53:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CR\",\"cs.DC\"]","methods":"[]","has_code":false}