用AI代理实现自适应联邦学习，解决客户端异构与系统波动问题
Agentic Federated Learning: The Future of Distributed Training Orchestration
- 引入语言模型代理，实现服务器端动态调度与客户端本地自主管理
- 代理可减少选择偏差，根据硬件自动调整模型复杂度和隐私预算
- 适合研究分布式协作、智能调度与去中心化系统的人士参考
尽管联邦学习（FL）承诺隐私保护与分布式协作，但其在真实场景中的有效性常受客户端随机异构性与不可预测系统动态的制约。现有静态优化方法难以适应这些波动，导致资源浪费与系统偏见。本文提出一种范式转变：基于语言模型的代理（LMagents）承担自主编排角色，构建代理型联邦学习（Agentic-FL）。服务器端代理通过上下文推理缓解选择偏差，客户端代理则作为本地守护者，动态管理隐私预算并根据硬件约束自适应调整模型复杂度。该框架不仅解决技术低效问题，更推动联邦学习向去中心化生态演进，实现自主协商协作，为激励驱动模型与算法公正奠定基础。本文探讨了代理可能产生的幻觉与安全挑战，并提出了构建鲁棒多代理系统的路线图。
原文摘要 · Abstract (English)
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.
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