Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices. Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence of faulty or adversarial updates. Trust based mechanisms are commonly used to mitigate these effects, yet most remain statistical and heuristic, relying on fixed parameters or simple adaptive rules that struggle to accommodate changing operating conditions. This paper presents a lightweight agentic trust coordination approach for FL in sustainable and resilient industrial networks. The proposed Agentic Trust Control Layer operates as a server side control loop that observes trust related and system level signals, interprets their evolution over time, and applies targeted trust adjustments when instability is detected. The approach extends prior adaptive trust mechanisms by enabling context aware intervention decisions, rather than relying on fixed or purely reactive parameter updates. By explicitly separating observation, reasoning, and action, the proposed framework supports stable FL operation without modifying client side training or increasing communication overhead.

翻译：工业网络中的分布式智能日益整合了异构资源受限设备上的感知、通信与计算能力。联邦学习（FL）能够在此类环境中实现协作式模型训练，但其可靠性受到客户端行为不一致、含噪感知条件以及存在错误或对抗性更新的影响。基于信任的机制常被用于缓解这些影响，但大多仍停留在统计与启发式层面，依赖固定参数或简单自适应规则，难以适应不断变化的工作条件。本文提出了一种轻量级智能体信任协调方法，用于可持续与弹性的工业网络中的联邦学习。所提出的智能体信任控制层作为服务器端控制回路运行，观察信任相关与系统级信号，解读其随时间演化的趋势，并在检测到不稳定时施加针对性的信任调整。该方法扩展了先前的自适应信任机制，支持基于上下文感知的干预决策，而非依赖固定或纯粹反应性的参数更新。通过明确分离观察、推理与行动，所提框架在不修改客户端训练过程或增加通信开销的前提下，支持稳定的联邦学习运行。