With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.
arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.
By Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
By Peyman Gholami, Hulya Seferoglu
arXiv:2608. 02222v1 Announce Type: new Abstract: One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in communication efficiency and privacy preservation.
By Zijian Jiang, Chaoli Sun, Handing Wang, Xilu Wang
Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data. However, heterogeneous local architectures often induce non-aligned representation spaces, making it difficult to transfer global knowledge across silos.
arXiv:2607. 09695v1 Announce Type: new Abstract: This paper addresses the challenging problem of dynamic feature drift in federated learning, where data distributions evolve across clients and over time -- a common scenario in real-world applications like financial technology.
By Kaijie Chen, Alex Johnson, Maria Garcia, Wei Zhang, Daniel Kim
arXiv:2607. 26801v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data.
By Zhaoyang Ma, Zhihao Wu, Xin Gao, Lipo Wang, Youfang Lin, Jing Wang
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada
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:2505. 19699v2 Announce Type: replace-cross Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy.
By Junming Liu, Yanting Gao, Yuqi Li, Siyuan Meng, Yifei Sun, Aoqi Wu, Yirong Chen, Ding Wang, Shiping Wen
arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.
By Su Wang, Mung Chiang, H. Vincent Poor
arXiv:2607. 29071v1 Announce Type: cross Abstract: Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parameter models.
By Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao, Mayi Xu, Quanqing Xu, Wei Ren, Qiang Yang, Yang Liu