Agentic Federated Learning: The Future of Distributed Training Orchestration
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 mana...
| Authors | Rafael O. Jarczewski, Gabriel U. Talasso, Leandro Villas et al. |
|---|---|
| Primary category | cs.MA |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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