[2604.04895] Agentic Federated Learning: The Future of Distributed Training Orchestration

Computer Science > Multiagent Systems arXiv:2604.04895 (cs) [Submitted on 6 Apr 2026] Title:Agentic Federated Learning: The Future of Distributed Training Orchestration Authors:Rafael O. Jarczewski, Gabriel U. Talasso, Leandro Villas, Allan M. de Souza View a PDF of the paper titled Agentic Federated Learning: The Future of Distributed Training Orchestration, by Rafael O. Jarczewski and Gabriel U. Talasso and Leandro Villas and Allan M. de Souza View PDF HTML (experimental) Abstract:Although Federated Learning (FL) promises privacy and distributed collaboration, its effectiveness in real-world scenarios is often hampered by the stochastic heterogeneity of clients and unpredictable system dynamics. Existing static optimization approaches fail to adapt to these fluctuations, resulting in resource underutilization and systemic bias. In this work, we propose a paradigm shift towards Agentic-FL, a framework where Language Model-based Agents (LMagents) assume autonomous orchestration roles. Unlike rigid protocols, we demonstrate how server-side agents can mitigate selection bias through contextual reasoning, while client-side agents act as local guardians, dynamically managing privacy budgets and adapting model complexity to hardware constraints. More than just resolving technical inefficiencies, this integration signals the evolution of FL towards decentralized ecosystems, where collaboration is negotiated autonomously, paving the way for future markets of incentive-based model...