The paper introduces Federated Agent Optimization (FAO), a framework for enabling large language model agents to improve collaboratively while keeping raw data, trajectories, and private knowledge local. FAO treats agent capabilities—such as memory, tools, rewards, skills, and structured knowledge—as a multi‑objective optimization space that balances utility, privacy leakage, and communication cost. It outlines methods for abstracting, protecting, aggregating, and adapting private experience into transferable capabilities, and highlights key challenges and future research directions for trustworthy federated agent systems.
By Qiang Yang, Zhiqiang Kou, Xueyi Zhang, Dong-Dong Wu, Hanlin Gu, Jing Guo, Yang Liu, Di Jiang, Qian Xu
arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.
By Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong
arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.
By Ahmed Elhussein, Florent Pollet, Gamze G\"ursoy
arXiv:2607. 16745v1 Announce Type: new Abstract: Multi-agent planning becomes substantially harder when agents must improve specialized decision-making skills while keeping their internal implementations private.
By Nguyen Viet Tuan Kiet, Bui Dinh Pham, Duong Quoc Chinh, Dao Van Tung, Tran Cong Dao, Huynh Thi Thanh Binh
arXiv:2604. 08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment.
By Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu
arXiv:2604.16778v3 Announce Type: replace-cross
Abstract: Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learni...
By Dixi Yao, Tahseen Rabbani, Manzil Zaheer, Tian Li
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
By Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.
By Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan, Guy Nagels
The paper introduces SAPE-FL, a personalization framework for Federated Learning that anchors each client’s model to both a global model and a similarity-weighted peer-averaged model. By applying dynamic, client-specific regularization based on model and output similarity, SAPE-FL balances global knowledge transfer with peer collaboration, filtering out dissimilar clients. The authors provide theoretical convergence guarantees and demonstrate empirically that SAPE-FL outperforms state‑of‑the‑art methods in highly heterogeneous and low‑data scenarios.
By Arun Kumar A V, Sunil Gupta, Dang Ngyuen, Bao Duong, Dat Phan Trong
arXiv:2505. 23593v4 Announce Type: replace Abstract: Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks.
By Nikita Agrawal, Ruben Mayer
arXiv:2609.35914v1 Announce Type: cross
Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sens...
By Deepthy K. Bhaskar, VP Binu, B Minimol
arXiv:2606. 03692v1 Announce Type: new Abstract: Recent AI agents can flexibly invoke skills to solve complex tasks, but their long-term improvement is fundamentally constrained by a lack of systematic skill construction, accumulation, and transfer.
By Yuan Xiong, Ziqi Miao, Qian Chen, Lijun Li, Yequan Wang, Shizhu He, Jun Zhao, Kang Liu