Federated learning is an approach to train machine learning models on the edge of the networks, as close as possible where the data is produced, motivated by the emerging problem of the inability to stream and centrally store the large amount of data produced by edge devices as well as by data privacy concerns. This learning paradigm is in need of robust algorithms to device heterogeneity and data heterogeneity. This paper proposes ModFL as a federated learning framework that splits the models into a configuration module and an operation module enabling federated learning of the individual modules. This modular approach makes it possible to extract knowlege from a group of heterogeneous devices as well as from non-IID data produced from its users. This approach can be viewed as an extension of the federated learning with personalisation layers FedPer framework that addresses data heterogeneity. We show that ModFL outperforms FedPer for non-IID data partitions of CIFAR-10 and STL-10 using CNNs. Our results on time-series data with HAPT, RWHAR, and WISDM datasets using RNNs remain inconclusive, we argue that the chosen datasets do not highlight the advantages of ModFL, but in the worst case scenario it performs as well as FedPer.
pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Saunders College of Business, Rochester Institute of Technology（罗切斯特理工学院商学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电子与计算机工程学院）
Personalized Federated Learning via Heterogeneous Modular Networks
The Pennsylvania State University（宾夕法尼亚州立大学）；NEC Laboratories America（美国NEC实验室）；Florida International University（佛罗里达国际大学）；AWS AI Labs, Amazon（亚马逊AWS人工智能实验室）
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Ecole de Technologie Superieure (ETS)（高等技术学院）；Khalifa University（哈利法大学）；Universite de Quebec a Trois-Rivieres (UQTR)（魁北克大学三河分校）；Lebanese American University（黎巴嫩美国大学）；New York University（纽约大学）
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Department of Radiology, University of Cambridge（放射学系，剑桥大学）
Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
Electrical Engineering and Computer Science, University of California at Merced（加州大学默塞德分校电气工程与计算机科学系）；Department of Applied Mathematics, University of California at Merced（加州大学默塞德分校应用数学系）；Department of Innovation and Research, North Carolina State University（北卡罗来纳州立大学创新与研究系）；Mechatronics, Embedded Systems and Automation (MESA) Lab, Department of Mechanical Engineering, School of Engineering, University of California, Merced（加州大学默塞德分校机械工程系机电一体化与嵌入式系统自动化实验室）
Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates
Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学）；Bielefeld University（比勒费尔德大学）；Center for Cybersecurity TTZ-WUE（TTZ-WUE网络安全中心）
FedReLa: Imbalanced Federated Learning via Re-Labeling
School of Computing and Information Systems, University of Melbourne, Victoria, Australia（墨尔本大学计算机与信息系统学院）；School of Statistics and Data Science, LPMC, KLMDASR, and LEBPS, Nankai University（南开大学统计与数据科学学院）；School of Mathematics and Statistics, University of Melbourne, Victoria, Australia（墨尔本大学数学与统计学学院）
Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉隆功大学国际技术学院）
Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing
Division of Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway（挪威癌症登记处，挪威公共卫生研究所，奥斯陆）；Department of Physics and Technology, The Arctic University of Norway, Tromsø, Norway（挪威极地大学物理与技术系）
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Jinan University（暨南大学）
Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning
Department of Computer Science and Engineering, European University of Bangladesh（欧洲 bangladesh 大学 计算机科学与工程系）；Department of Computer Science and Engineering , BRAC University,Dhaka, Bangladesh（brac 大学 计算机科学与工程系）；Department of Computer Science and Engineering , Daffodil International University, Dhaka, Bangladesh（daffodil 国际大学 计算机科学与工程系）；Department of Institute of Information Technology, University of Dhaka, Bangladesh（达卡大学 信息科技学院）；Department of Computer Science and Engineering , BRAC University,Dhaka（brac 大学 计算机科学与工程系）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（ochsner 成果研究中心，ochsner 研究）
Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR
University at Buffalo–SUNY（布法罗大学-纽约州立大学）；Department of Electrical Engineering（电气工程系）；New Jersey Institute of Technology (NJIT)（新泽西理工学院）
Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
University at Buffalo –SUNY, Department of Electrical Engineering（布法罗大学–纽约州立大学，电气工程系）；Adobe Research（Adobe研究）；University of California – San Diego, Department of Electrical and Computer Engineering（加州大学圣地亚哥分校，电气与计算机工程系）；NC State University, Department of Computer Science（北卡罗来纳州立大学，计算机科学系）
Perfectly-Private Analog Secure Aggregation in Federated Learning
Department of Electrical Engineering, Chalmers University of Technology, Sweden（瑞典查尔姆斯理工大学电子工程系）；Department of Mathematics and Systems Analysis, Aalto University, Finland（芬兰阿尔托大学数学与系统分析系）
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
University of Hamburg（汉堡大学）；Sapienza University of Rome（罗马第一大学）；University of Campania Luigi Vanvitelli（坎帕尼亚大学路易吉·万维泰利）；Semmelweis University（塞麦尔维斯大学）；SC Egnosis SRL（SC Egnosis公司）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经学研究所）；LMU München（慕尼黑大学）；LMU Hospital（慕尼黑大学医院）；Research Institute – Digital Human Rights Center（数字人权中心研究所）；Medical University of Vienna（维也纳医科大学）；SBA Research gGmbH（SBA研究中心）；Joslin Diabetes Center（乔斯林糖尿病中心）；Harvard Medical School（哈佛医学院）；Karolinska Institutet（卡罗林斯卡学院）；Södersjukhuset（南斯德哥尔摩综合医院）；University College Cork（科克大学学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；Mass General Brigham（麻省总布里格姆医院）；University of Southern Denmark（南丹麦大学）；Université Paris-Saclay（巴黎-萨克雷大学）；INRAE（法国国家农业、食品与环境研究院）
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Department of Electrical Engineering, Chalmers University of Technology（查尔姆斯理工大学电子工程系）；Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）；Department of Electrical Engineering, Yale University（耶鲁大学电子工程系）
Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity
Rajshahi University of Engineering & Technology（拉杰沙希工程与技术大学）；North South University（北南大学）；Missouri State University（密苏里州立大学）；Malardalen University（梅拉达伦大学）；American International University-Bangladesh（孟加拉国美国国际大学）
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
University of Southern California（南加利福尼亚大学）；Mediatek Inc.（联发科技股份有限公司）；The Hong Kong University of Science and Technology（香港科技大学）；Princeton University（普林斯顿大学）；University of California at Riverside（加州大学河滨分校）
