LLM agents are widely deployed in complex interactive tasks, yet privacy constraints often preclude centralized optimization and co-evolution across dynamic environments. Despite the demonstrated success of Federated Learning (FL) on static datasets, its effectiveness in open-ended, self-evolving agent systems remains largely unexplored. In such settings, the direct application of standard FL is particularly challenging, as heterogeneous tasks and sparse, trajectory-level reward signals give rise to severe gradient instability, which undermines the global optimization process. To bridge this gap, we propose Fed-SE, a Federated Self-Evolution framework for LLM agents that establishes a local evolution-global aggregation paradigm. Locally, agents employ parameter-efficient fine-tuning on filtered, high-return trajectories to achieve stable gradient updates. Globally, Fed-SE aggregates updates within a low-rank subspace, reducing communication cost across clients. Experiments across five heterogeneous environments demonstrate that Fed-SE improves average task success rates by 10\% over the state-of-the-art FedIT, validating its effectiveness in cross-environment knowledge transfer under privacy constraints.

翻译：LLM智能体已广泛应用于复杂的交互式任务，然而隐私约束往往阻碍了跨动态环境的集中式优化与协同进化。尽管联邦学习在静态数据集上已取得显著成功，但其在开放式自进化智能体系统中的有效性仍很大程度上未被探索。在此类场景中，直接应用标准联邦学习面临特殊挑战：异构任务与稀疏的轨迹级奖励信号会导致严重的梯度不稳定性，从而破坏全局优化过程。为弥补这一空白，我们提出Fed-SE——一种面向LLM智能体的联邦自进化框架，该框架建立了局部进化-全局聚合的范式。在局部层面，智能体通过对筛选出的高回报轨迹进行参数高效微调，实现稳定的梯度更新。在全局层面，Fed-SE将更新量聚合于低秩子空间内，降低了跨客户端的通信成本。在五个异构环境中的实验表明，Fed-SE相较于最先进的FedIT方法将平均任务成功率提升了10%，验证了其在隐私约束下跨环境知识迁移的有效性。