Non-IID Federated Learning

Follow this topic Ask about this topic

Non-IID federated learning studies how clients can train a shared model when their data differ in labels, features, domains, or changing conditions, while keeping raw data local. Methods include optimization corrections such as FedProx and SCAFFOLD, clustering and personalization, and knowledge distillation or prototypes to share useful information without simply averaging every update. Client selection, compression, privacy mechanisms, and asynchronous or decentralized training address practical limits such as communication, device capacity, and unreliable participation. A central tension remains between a broadly useful global model and accurate, fair models for individual clients; gains depend on the kind of heterogeneity and must be weighed against communication, privacy, and worst-client performance.

Latest papers

211 papers17 papers in the last four weeks, down 37% on the four weeks before. 0.1% of all new papers.

Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery
Mar 8, 2026Luyao Zou, Fei Pan, Jueying Li +4Federated Knowledge Distillation Remote Sensing Image Understanding
College of Computing and Informatics, Sungkyunkwan University, South Korea · Department of Computer Science and Engineering, University of Michigan, United States · Konkuk Aerospace Design-Airworthiness Institute, Konkuk University, Seoul, 05029, South Korea +3
Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
Mar 8, 2026Pu Qiu, Chen Ouyang, Yongyang Xiong +3Error Feedback Federated Learning
School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, P.R China. · Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China. · Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
CA-AFP: Cluster-Aware Adaptive Federated Pruning
Mar 2, 2026Om Govind Jha, Harsh Shukla, Haroon R. LoneFederated Learning Neural Network Pruning
IISER Bhopal Bhopal, India
DeepAFL: Deep Analytic Federated Learning
Feb 28, 2026Jianheng Tang, Yajiang Huang, Kejia Fan +8Gradient-Free Optimization Federated Learning
Peking University, PRC · Key Lab of High Confidence Software Technologies (PKU), Ministry of Education, PRC · Central South University, PRC +4
FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
Feb 28, 2026Xianxun Zhu, Zezhong Sun, Imad Rida +4Multimodal Federated Learning Multimodal Sentiment Analysis
Shanghai University, China · Nanyang Technological University, Singapore · Macquarie University, Australia +3
FedNSAM:Consistency of Local and Global Flatness for Federated Learning
Feb 27, 2026Junkang Liu, Fanhua Shang, Yuxuan Tian +2Federated Learning Sharpness-Aware Minimization
College of Intelligence and Computing, Tianjin University, Tianjin, China · College of Management and Economics, Tianjin University, Tianjin, China · Medical College, Tianjin University; Peng Cheng Laboratory, Tianjin, China +1
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
Feb 26, 2026Anik Pramanik, Murat Kantarcioglu, Vincent Oria +1Clustered Federated Learning Federated Learning
New Jersey Institute of Technology, USA · Virginia Tech, USA
FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
Feb 24, 2026Alina Devkota, Jacob Thrasher, Donald Adjeroh +2Federated Learning Federated Learning Aggregation
West Virginia University · University of Aberdeen · Fogsphere (Redev.AI)
Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation
Feb 24, 2026Sales Aribe, Gil Nicholas CagandeFederated Learning Communication-Efficient Distributed Training
Information Technology Department, Bukidnon State University, Malaybalay City, Philippines
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Feb 24, 2026Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt +1Client Selection in Federated Learning Heterogeneous Federated Learning
University of Oregon, US · Boston College, US · Microsoft Research, India
Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data
Feb 22, 2026Junkang Liu, Fanhua Shang, Hongying Liu +3Federated Learning Second-Order Optimization
College of Intelligence and Computing, Tianjin University, Tianjin, China · Medical School, Tianjin University, Tianjin, China · Peng Cheng Lab, Shenzhen, China +2
FedZMG: Efficient Client-Side Optimization in Federated Learning
Feb 20, 2026Fotios Zantalis, Evangelos Zervas, Grigorios KoulourasFederated Learning Non-IID Federated Learning
TelSiP Research Laboratory, Department of Electrical and Electronic Engineering, School of Engineering, University of West Attica, Ancient Olive Grove Campus, 250 Thivon Str., GR-12241 Athens, Greece
Fractional-Order Federated Learning
Feb 17, 2026Mohammad Partohaghighi, Roummel Marcia, YangQuan ChenFederated Learning Federated Learning Aggregation
Electrical Engineering and Computer Science, University of California at Merced, USA · Department of Applied Mathematics, University of California Merced, Merced, CA, USA · Mechatronics, Embedded Systems and Automation (MESA) Lab, Department of Mechanical Engineering, School of Engineering, University of California, Merced, CA 95343, USA
pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI
Feb 16, 2026Qingqian Yang, Hao Wang, Sai Qian Zhang +6Federated Learning Aggregation Non-IID Federated Learning
Stevens Institute of Technology · Hoboken, NJ, USA · New York University +9
An Adaptive Differentially Private Federated Learning Framework
Feb 6, 2026Jin Wang, Hui Ma, Yajun Zhang +4Non-IID Federated Learning Differentially Private Federated Learning
Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi 830017, China · College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China · Jilin University, Changchun 130012, China
Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
Dec 28, 2025Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail +2Federated Learning Aggregation Non-IID Federated Learning
Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan.
How (Mis)calibrated is your Federated CLIP and what to do about it?
Dec 3, 2025Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3Vision-Language Models VLM Adaptation
University of Trento, Italy · ´Ecole de technologie sup´erieure, QC, Canada · AMIAD, Pˆole Recherche, Palaiseau, France +2
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning
Nov 30, 2025Haozhe WuMultimodal Federated Learning Non-IID Federated Learning
School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China
FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements
Oct 14, 2025Xiao Yang, Dengbo He, Duoxun Tang +2Federated Domain Generalization Federated Learning
Systems Hub, the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning
Sep 17, 2025Zhanting Zhou, Zeyu Ma, Ziqiang Zheng +1Federated Learning Aggregation Domain Generalization
University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Sep 5, 2025Jiaojiao Zhang, Yizhao Fan, Yuqi Xu +1Communication-Efficient Distributed Training Non-IID Federated Learning
Intelligent Computing Research Center, Great Bay University, Dongguan, China · Center for Machine Learning Research, Peking University, Beijing, China
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
Aug 16, 2025You Hak Lee, Keming Fan, Xiaofan Yu +4Compute-in-Memory Federated Learning Aggregation
University of California San Diego, USA
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Jul 24, 2025Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +3Asynchronous Federated Learning Non-IID Federated Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China · Sun Yat-sen University, School of Computer Science and Engineering, Guangzhou 510275, China
Data-Driven Laplacian-Constrained Personalized Federated Learning
May 12, 2025Samuel Erickson, Mikael JohanssonFederated Learning Non-IID Federated Learning
Division of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
Dec 9, 2024Jiechao Gao, Yuangang Li, Jie Wang +3Hierarchical Federated Learning Internet of Things
University of Virginia Charlottesville, VA, USA
Influence-Oriented Personalized Federated Learning
Oct 4, 2024Yue Tan, Guodong Long, Jing Jiang +1Federated Learning Aggregation Non-IID Federated Learning
Griffith University · University of Technology Sydney · Hong Kong Polytechnic University
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
Sep 9, 2024Jianyi Zhang, Hao Frank Yang, Ang Li +5Multimodal Federated Learning Non-IID Federated Learning
Duke University · Johns Hopkins University · University of Maryland College Park +1
Personalized Additive Modeling for Multi-level Federated Learning
May 26, 2024Shutong Chen, Guodong Long, Tianyi Zhou +3Non-IID Federated Learning Federated Learning
University of Technology Sydney, Australia · Mohamed bin Zayed University of Artificial Intelligence, UAE · The Hong Kong Polytechnic University, China
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Nov 6, 2023Yipeng Li, Xinchen LyuNon-IID Federated Learning Stochastic Optimization Convergence
National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing, 100876, China
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Jun 22, 2023Tianyu Zhao, Junping Du, Yingxia Shao +1Federated Learning Aggregation Clustering
Beijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China

Mar 8, 2026J/K move · Enter open · S save

Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery

Luyao Zou, Fei Pan, Jueying Li, Yan Kyaw Tun+3

College of Computing and Informatics, Sungkyunkwan University, South Korea · Department of Computer Science and Engineering, University of Michigan, United States · Konkuk Aerospace Design-Airworthiness Institute, Konkuk University, Seoul, 05029, South Korea+3

Federated Knowledge Distillation Remote Sensing Image Understanding Non-IID Federated Learning

Open paper AskPDF arXiv

Federated learning (FL) has recently become a promising solution for analyzing remote sensing satellite imagery (RSSI). However, the large scale and inherent data heterogeneity of images collected from multiple satellites, where the local data distribution of each satellite differs from the global one, present significant challenges to effective model training. To address this issue, we propose a Geometric Knowledge-Guided Federated Dual Knowledge Distillation (GK-FedDKD) framework for RSSI analysis. In our approach, each local client first distills a teacher encoder (TE) from multiple student encoders (SEs) trained with unlabeled augmented data. The TE is then connected with a shared classifier to form a teacher network (TN) that supervises the training of a new student network (SN). The intermediate representations of the TN are used to compute local covariance matrices, which are aggregated at the server to generate global geometric knowledge (GGK). This GGK is subsequently employed for local embedding augmentation to further guide SN training. We also design a novel loss function and a multi-prototype generation pipeline to stabilize the training process. Evaluation over multiple datasets showcases that the proposed GK-FedDKD approach is superior to the considered state-of-the-art baselines, e.g., the proposed approach with the Swin-T backbone surpasses previous SOTA approaches by an average 68.89% on the EuroSAT dataset.