arXiv Computer Vision October 2, 2026 By Bar{\i}\c{s} B\"uy\"ukta\c{s}, Beg\"um Demir

FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

arXiv Machine Learning

Aug 11

FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

arXiv:2608. 09687v1 Announce Type: new Abstract: Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry.

By Satwat Bashir, Tasos Dagiuklas, Muddesar Iqbal

benchmarks

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 15

FedSPC: Shared Parameter Correction for Personalized Federated Learning

arXiv:2606. 13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation.

By Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Toru Hirano

arXiv Machine Learning

Jul 21

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).

By Ferdous Pervej, Minseok Choi, Andreas F. Molisch

computer-vision efficiency

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 AI

Aug 10

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.

By Majid Kundroo, Tinku Singh, Taehong Kim

safety

One email a morning, machine-written

Related stories

FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

Federated Learning for Feature Generalization with Convex Constraints

FedSPC: Shared Parameter Correction for Personalized Federated Learning

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

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

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks