Federated Learning (FL) is a decentralized paradigm that enables a client-server architecture to collaboratively train a global Artificial Intelligence model without sharing raw data, thereby preserving privacy. A key challenge in FL is Non-IID data. Quantity Skew (QS) is a particular problem of Non-IID, where clients hold highly heterogeneous data volumes. Clustered Federated Learning (CFL) is an emergent variant of FL that presents a promising solution to Non-IID problem. It improves models' performance by grouping clients with similar data distributions into clusters. CFL methods generally fall into two operating strategies. In the first strategy, clients select the cluster that minimizes the local training loss. In the second strategy, the server groups clients based on local model similarities. However, most CFL methods lack systematic evaluation under QS but present significant challenges because of it. In this paper, we present two main contributions. The first one is an evaluation of state-of-the-art CFL algorithms under various Non-IID settings, applying multiple QS scenarios to assess their robustness. Our second contribution is a novel iterative CFL algorithm, named CORNFLQS, which proposes an optimal coordination between both operating strategies of CFL. Our approach is robust against the different variations of QS settings. We conducted intensive experiments on six image classification datasets, resulting in 270 Non-IID configurations. The results show that CORNFLQS achieves the highest average ranking in both accuracy and clustering quality, as well as strong robustness to QS perturbations. Overall, our approach outperforms actual CFL algorithms.
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
Graduate School of Science and Technology, University of Tsukuba（茨川大学理学技术研究生院）；Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba（茨川大学先进研究所人工智能研究中心）；Institute of Systems and Information Engineering, University of Tsukuba（茨川大学系统与信息工程研究所）
On the Convergence of Clustered Federated Learning
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；University of Washington, Seattle（西雅图华盛顿大学）；University of Maryland, College Park（马里兰大学帕克分校）
Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning
Department of Computer, Control；Management Engineering Sapienza University of Rome Rome, Italy；Telef\'onica Research Barcelona, Spain；Keysight AI Labs Barcelona, Spain
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea（人工智能跨学科项目，首尔国立大学，韩国）；Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea（核医学系，首尔国立大学医学院，韩国）；Brightonix Imaging Inc. Seoul, Republic of Korea（Brightonix影像公司，首尔，韩国）
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University（数据科学与人工智能系，IT学院和莫纳什能源研究所，莫纳什大学）；School of Computer Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, and Shenzhen Institute for Advanced Study of UESTC, Shenzhen, China（计算机科学与技术学院，电子科技大学（UESTC）成都校区，以及UESTC深圳高级研究所，深圳，中国）
Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models
1 School of Artificial Intelligence；Computer Science, Jiangnan University 2 Antai College of Economics \& Management, Shanghai Jiao Tong University 3 School of Computer Science, Shanghai Jiao Tong University 4 Cloud Computing；Distributed Systems Laboratory, University of Melbourne
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
School of Electrical and Electronic Engineering, The University of Manchester（曼彻斯特大学电气与电子工程学院）；Khalifa University（哈利法大学）；Department of Electrical Engineering and Computer Science, Khalifa University（哈利法大学电气工程与计算机科学系）；Department of Electrical and Electronic Engineering, University of Manchester（曼彻斯特大学电气与电子工程学院）
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
School of Computing, Southern Illinois University（南伊利诺伊大学计算机学院）；Security, Privacy and Intelligence for Edge Devices Laboratory (SPEED Lab)（边缘设备安全、隐私与智能实验室（SPEED Lab））
Distilling A Universal Expert from Clustered Federated Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼大学技术学院澳大利亚人工智能研究所）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机科学学院）
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China（浙江工业大学信息工程学院，杭州 310023，中国）；Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems, Hangzhou, China（浙江省复杂系统智能感知与控制重点实验室，杭州，中国）；College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China（浙江大学控制科学与工程学院，杭州 310027，中国）；Computing Science and Internet of Things, Newcastle University, NE1 7RU Newcastle, UK（新castle大学计算科学与物联网学院，NE1 7RU Newcastle, UK）
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；IHPC, A*STAR（新加坡科技研究局信息通信研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Guangxi Medical University（广西医科大学）；National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国学院）
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Washington State University（华盛顿州立大学）；Princeton University（普林斯顿大学）；IBM Research（IBM研究院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣迭戈分校）
FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments
Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan（计算机科学系，拉合尔管理科学大学，拉合尔，巴基斯坦）
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT
Information Countermeasure Technique Institute（信息对抗技术研究所）；School of Cyberspace Science（网络空间科学学院）；Faculty of Computing（计算学部）；Harbin Institute of Technology（哈尔滨工业大学）
Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data
Department of Computer Science, University of California, Santa Barbara, CA, USA（计算机科学系，加州大学圣芭芭拉分校）；Department of Electrical and Electronic Engineering, The University of Hong Kong（电气与电子工程系，香港大学）；Department of Electronics and Information Engineering, Shenzhen University（电子与信息工程系，深圳大学）
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉法叶分校计算与信息学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；University of Delaware（特拉华大学）
Auction Based Clustered Federated Learning in Mobile Edge Computing System
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Cyberspace Security Research Center, Peng Cheng Laboratory（鹏城实验室网络空间安全研究中心）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电气与数据工程学院）；Department of Computer Science and Technology, Fuzhou University（福州大学计算机科学与技术学院）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology（吕勒奥理工大学计算机、电气与空间工程系）
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
Department of ECE and INMC, Seoul National University, Seoul, South Korea（首尔国立大学电子与信息科学系及网络信息中心，首尔，韩国）；Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，首尔，韩国）；Department of Computer Science, University of Colorado Boulder, CO, USA（科罗拉多大学博尔德分校计算机科学系，科罗拉多州，美国）
A Joint Gradient and Loss Based Clustered Federated Learning Design
University of Miami（迈阿密大学）；Frost Institute for Data Science and Computing, University of Miami（迈阿密大学弗罗斯特数据科学与计算研究所）；Zhejiang University（浙江大学）；North Carolina State University（北卡罗来纳州立大学）
UA-PDFL: A Personalized Approach for Decentralized Federated Learning
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation (Jiangnan University)（江南大学江苏省人机协同软件与媒体技术重点实验室）
