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arXiv 2604.04895cs.MAcs.AI
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代理联邦学习:分布式训练编排的未来

Agentic Federated Learning: The Future of Distributed Training Orchestration

Institute of Computing, University of Campinas(坎皮纳斯州立大学计算研究所);Hub of Artificial Intelligence and Cognitive Architectures - H.IAAC(人工智能与认知架构中心 - H.IAAC)

Rafael O. Jarczewski, Gabriel U. Talasso, Leandro Villas, Allan M. de Souza

AI总结:

本文提出代理联邦学习框架,通过语言模型代理自主协调,缓解客户端异质性和系统动态波动,提升资源利用和公平性,推动联邦学习向去中心化生态系统演进。

摘要:

尽管联邦学习(FL)承诺隐私和分布式协作,但其在现实场景中的效果常受限于客户端的随机异质性和不可预测的系统动态。现有静态优化方法难以适应这些波动,导致资源利用率低和系统偏见。本文提出代理联邦学习(Agentic-FL)框架,其中基于语言模型的代理(LMagents)承担自主协调角色。不同于刚性协议,我们展示了服务器端代理如何通过上下文推理缓解选择偏见,而客户端端代理则作为本地守护者,动态管理隐私预算并适应硬件约束。这不仅解决了技术效率问题,还标志着联邦学习向去中心化生态系统演进,为未来基于激励的模型和算法正义开辟道路。我们讨论了该方法的可靠性(幻觉)和安全挑战,概述了在联邦环境中构建稳健多代理系统的路线图。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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