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:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.
By Ahmed Sayeed Faruk, Mohammad Shahverdikondori, Elena Zheleva
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
By Zahra Kharaghani, Ali Dadras, Tommy L\"ofstedt
arXiv:2607. 01474v1 Announce Type: new Abstract: Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard centralized techniques due to privacy and heterogeneity constraints.
By Haemin Park, Diego Klabjan, Martin W. Braun, Xiuqi Li, Balakrishnan Ananthanarayanan
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:2609.36660v1 Announce Type: new
Abstract: We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients witho...
By Leonardo F. Toso, James Anderson, Rafael Pinot, Nirupam Gupta
Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tackle this issue, centered clipping and Huber aggregators have been exploited for Byzantine robustness.
arXiv:2609.07312v1 Announce Type: new
Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attack...
By Xiao Ma, Hong Shen, Hui Tian, Wenqi Lyu, Wei Ke
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
arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.
By Yutong He, Zhengyang Huang, Jiahe Geng, Kun Yuan
arXiv:2606. 30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model.
By Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich
arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.
By Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen