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.

翻译：尽管联邦学习（FL）承诺实现隐私保护与分布式协作，但在实际场景中其有效性常受制于客户端的随机异质性和不可预测的系统动态。现有的静态优化方法无法适应这些波动，导致资源利用不足与系统性偏差。本文提出一种范式转换——主体化联邦学习（Agentic-FL）框架，其中基于语言模型的智能体（LMagents）承担自主编排角色。与僵化协议不同，我们展示了服务器端智能体如何通过上下文推理缓解选择偏差，而客户端智能体则作为本地守护者动态管理隐私预算，并根据硬件约束调整模型复杂度。这种集成不仅解决了技术低效问题，更标志着联邦学习向去中心化生态系统演进——协作通过自主协商达成，为未来基于激励的模型市场与算法公正铺平道路。我们讨论了该方法的可靠性（幻觉现象）与安全挑战，并提出了面向联邦环境中鲁棒多智能体系统的路线图。