arXiv:2606.03143v1 Announce Type: cross Abstract: 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.
The accelerating development and deployment of LLM agents make their efficient and privacy-preserving evolution a critical concern for both performance and user adoption.
This work directly addresses the dual challenges of data scarcity for agent skill development and user privacy, which are key bottlenecks for the widespread adoption and trusted operation of AI agents.
The proposed framework enables collaborative agent skill development without centralizing sensitive user data, potentially accelerating agent capabilities while adhering to privacy principles.
- · AI agent developers
- · Cloud service providers (serving agents)
- · Privacy-focused AI platforms
- · Users of AI agents
- · Centralized data aggregators (for agent training)
- · Less privacy-aware agent development platforms
Federated learning becomes a standard paradigm for agent skill evolution, enabling more robust and generalized AI agents.
Increased trust in AI agents due to inherent privacy protections leads to broader deployment in sensitive applications.
The proliferation of highly capable, privacy-preserving agents could disrupt industries reliant on proprietary data, shifting value towards collaborative, distributed AI development.
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Read at arXiv cs.CL