新的联邦学习方法解决了效率和泛化问题

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Yuchen Hou, Yongshan Chen, Zhuowen Zou, Calvin Yeung, Mohsen Imani, Tian Lan, Mahdi Imani · 2026-05-29 04:00

   FedQHD: Closed-Form Function-Space Federated Reinforcement Learning

   arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style parameter averaging is not function-space consistent: wh…

2. arXiv cs.LG TIER_1 English(EN) · Zhen Li, Jun Cai, Chao Yang, Haoran Gao · 2026-05-26 04:00

   Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning

   arXiv:2605.25916v1 Announce Type: new Abstract: Federated edge learning (FEEL) has recently emerged as a promising paradigm for achieving edge intelligence (EI) via enabling collaborative model training across edge devices while protecting data privacy. In this paper, we put fort…

3. arXiv cs.LG TIER_1 English(EN) · Haoran Gao · 2026-05-25 14:51

   Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning

   Federated edge learning (FEEL) has recently emerged as a promising paradigm for achieving edge intelligence (EI) via enabling collaborative model training across edge devices while protecting data privacy. In this paper, we put forth an online optimization framework that jointly …

4. arXiv cs.CV TIER_1 English(EN) · Yuting Ma, Lechao Cheng, Xiaohua Xu · 2026-05-28 04:00

   Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning

   arXiv:2605.27900v1 Announce Type: new Abstract: Federated Learning (FL) with pre-trained Vision-Language Models (VLMs) has emerged as a promising paradigm for various downstream tasks. By leveraging its strong representations, recent studies improve task adaptation under insuffic…