# FederatedSkill: Federated Learning for Agentic Skill Evolution

arXiv: https://arxiv.org/abs/2606.03143 · HTML: https://mlsanity.com/paper/2606.03143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning](https://mlsanity.com/paper/2602.08234/) (2026)
- [MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents](https://mlsanity.com/paper/2602.02474/) (2026)
- [Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization](https://mlsanity.com/paper/2402.17574/) (2024)
- [SkillClaw: Let Skills Evolve Collectively with Agentic Evolver](https://mlsanity.com/paper/2604.08377/) (2026)
- [Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality](https://mlsanity.com/paper/2602.08004/) (2026)
- [How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings](https://mlsanity.com/paper/2604.04323/) (2026)
- [SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks](https://mlsanity.com/paper/2604.20087/) (2026)
- [SkillEvolver: Skill Learning as a Meta-Skill](https://mlsanity.com/paper/2605.10500/) (2026)
- [Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents](https://mlsanity.com/paper/2603.07915/) (2026)
- [WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks](https://mlsanity.com/paper/2510.06587/) (2025)
- [Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents](https://mlsanity.com/paper/2512.08870/) (2025)

## Built on by (most cited first, 6 of 6)

- [EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement](https://mlsanity.com/paper/2608.04968/) (2026)
- [Skillware: A Software Ontology and Engineering Lifecycle for Persistent Behavioral Artifacts](https://mlsanity.com/paper/2607.18970/) (2026)
- [Query-Only Backdoor Attacks on Self-Evolving Skills via Trajectory Poisoning](https://mlsanity.com/paper/2608.08303/) (2026)
- [Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents](https://mlsanity.com/paper/2609.32511/) (2026)
- [Learning from Research: Toward Lifelong Agent Harness Evolution](https://mlsanity.com/paper/2609.40169/) (2026)
- [Federated Agent Optimization](https://mlsanity.com/paper/2610.01195/) (2026)

## Abstract (arXiv)

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.