arXiv Machine Learning

Differentiated Aggregation to Improve Generalization in Federated Learning

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.

arXiv Machine Learning

Oct 3

Latent Information Sharing for Accelerating Federated Learning

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

safety

arXiv Machine Learning

Jun 15

Federated Learning for Feature Generalization with Convex Constraints

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

benchmarks

arXiv Machine Learning

Jun 10

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

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 Machine Learning

Jun 16

Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data

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 Machine Learning

Jun 30

Personalized Additive Modeling for Multi-level Federated Learning

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

arXiv AI

Aug 18

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.

By Hai Anh Tran, Cuong Ta, Truong X. Tran

safety

arXiv AI

Jun 16

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

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

safety

arXiv AI

Jul 10

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.

By Lingyu Qiu, Daniela Annunziata, Stefano Izzo, Fabio Giampaolo, Francesco Piccialli

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 7

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.

By Liyang Yuan, Yibo Yang, Dandan Guo

benchmarks

arXiv Machine Learning

Oct 12

FedPCD: Priority-Constrained Federated Aggregation

FedPCD introduces a priority-constrained federated optimization framework that decouples the global aggregation objective from client protection. By preserving the standard federated aggregate and applying only minimal corrections to satisfy client performance constraints, FedPCD achieves higher global accuracy and improved worst‑10% client accuracy across heterogeneous benchmarks. The method also maintains competitive trade‑offs between global accuracy and client variability while converging faster with fewer communication rounds.

By Yousef Irshaid, Dara Varam, Mohamed I. AlHajri

benchmarks safety

arXiv AI

Jun 30

Federated Learning with Energy-Based Structured Probabilistic Inference

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 Machine Learning

Aug 13

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

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

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