让智能体在保护隐私的前提下，协作进化出更高效的能力。
FederatedSkill: Federated Learning for Agentic Skill Evolution
- 用语义技能差异作为通信核心，替代原始轨迹共享
- 20类任务测试中成功率提升44.4%，计算成本降37.5%
- 支持个性化能力演化，适合异构用户场景
现代大模型智能体越来越依赖技能库来处理复杂任务，技能演化成为自我改进的核心驱动力。然而，单一用户的任务流缺乏多样性，难以构建全面技能。跨用户协作可缓解数据瓶颈，但现有轨迹共享方法损害用户隐私，并生成统一的全局技能库，无法适应客户端异质性。我们提出FederatedSkill，一种隐私保护的协作式智能体演化框架。不同于直接共享原始轨迹，FederatedSkill以语义技能差分为基本通信单元，对本地技能库进行结构化更新。服务器端的演化代理聚合这些补丁，动态建模各客户端的能力边界，实现严格个性化的技能演化，而非次优的全局平均。在20个不同的智能体任务族上评估显示，FederatedSkill显著优于自演化基线，在成功率上最高提升44.4%，计算成本降低37.5%。
原文摘要 · Abstract (English)
Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement. However, isolated single-user task streams lack the diversity required to build comprehensive skills. While cross-user collaboration can overcome this data bottleneck, current trajectory-sharing approaches compromise user privacy and impose a uniform global library that fails to accommodate client heterogeneity. We introduce FederatedSkill, a privacy-preserving framework for collaborative agent evolution. Moving beyond raw trajectory sharing, FederatedSkill utilizes semantic skill diffs, structured patches over local libraries, as the fundamental unit of communication. On the server side, an evolution agent aggregates these patches to dynamically model client-specific capability boundaries, facilitating strictly personalized skill evolution rather than a suboptimal global average. Evaluated across 20 distinct agent task families, FederatedSkill demonstrates substantial gains over self-evolving baselines, achieving up to a 44.4% increase in success rate and a 37.5% reduction in computational cost.
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