Agentic Federated Learning: The Future of Distributed Training Orchestration

TL;DR

This paper introduces Agentic Federated Learning, where autonomous language model-based agents dynamically manage and optimize distributed training, addressing heterogeneity and system unpredictability.

Contribution

It proposes a novel multi-agent framework for federated learning that enhances adaptability, resource utilization, and bias mitigation through autonomous server and client agents.

Findings

Server-side agents mitigate selection bias via contextual reasoning.

Client-side agents manage privacy budgets and adapt to hardware constraints.

Framework signals a shift towards decentralized, autonomous federated ecosystems.

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 models and algorithmic justice. We discuss the reliability (hallucinations) and security challenges of this approach, outlining a roadmap for resilient multi-agent systems in federated environments.