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cs.AI2026
FedSkillGit: Federated Skill Evolution via Version Control
Boxun Lan, Jiamei Li, Weijian Li +5
Large language model agents can improve their capabilities by evolving reusable natural-language skills from execution experience. In real-world deployments, this experience is dis…
cs.LG2026
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
Zhuang Qi, Ying-Peng Tang, Lei Meng +4
Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representative samples from past tasks. E…
cs.LG2026
FedPDPO: Federated Personalized Direct Preference Optimization for Large Language Model Alignment
Kewen Zhu, Liping Yi, Zhiming Zhao +3
Aligning large language models (LLMs) with human preferences in federated learning (FL) is challenging due to decentralized, privacy-sensitive, and highly non-IID preference data.…