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:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.
By Xu Zhang, Wenpeng Li, Yunfeng Shao, Yonglin Liu, Kaiwen Zhou, Yinchuan Li
StoCFL is a clustered federated learning framework designed to address Non-IID data and dynamic client participation. It introduces a flexible clustering mechanism that allows arbitrary client participation and accommodates newly joined clients, improving data efficiency and model performance. Experiments on four Non-IID settings and a real-world dataset demonstrate that StoCFL achieves promising cluster results even when the number of clusters is unknown, outperforming baseline approaches across various scenarios.
By Dun Zeng, Xiangjing Hu, Shiyu Liu, Yue Yu, Qifan Wang, Zenglin Xu
arXiv:2609.39250v1 Announce Type: new
Abstract: Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a...
By Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park
arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).
By Randeep Bhatia, Nikos Papadis, Murali Kodialam, TV Lakshman, Sayak Chakrabarty
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillat...
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay
Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a prob...
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: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
arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.
By Shutong Chen, Guodong Long, Tianyi Zhou, Jie Ma, Jing Jiang, Chengqi Zhang
FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.
By Wenxuan Ye, Onur Ayan, Xueli An, Georg Carle