Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heterogeneity of data distribution among clients usually leads to poor model inference. In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process. In the last iteration, the server aggregates the prototypes transmitted from distributed clients and then sends them back to local clients for their respective model inferences. Experiments on two baseline datasets show that our proposal can achieve higher accuracy (at least 1%) and relatively efficient communication than two popular baselines under different heterogeneous settings.
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
Department of Computer Science and Engineering, School of Computing, Kyung Hee University（计算机科学与工程系，计算学院，庆熙大学）；Department of Artificial Intelligence, School of Computing, Kyung Hee University（人工智能系，计算学院，庆熙大学）；Digital Science and Technology Institute, The University of Danang—Vietnam-Korea University of Information and Communication Technology（数字科学与技术研究所，丹那大学—越南-韩国信息与通信技术大学）
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，计算机科学与工程学院，北京航空航天大学，北京，中国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）；Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo, USA（人工智能商业创新中心，管理科学与系统系，管理学院，布法罗大学，美国）
Robust Federated Learning on Edge Devices with Domain Heterogeneity
Department of Computer Science and Engineering, Kyung Hee University, 171-04, Republic of Korea（韩国庆熙大学计算机科学与工程系）；Machine learning department, Mohamed Bin Zayed University of Artificial Intelligence, United Arab Emirates（阿联酋穆罕默德·本·扎耶德人工智能大学机器学习系）
RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
College of Intelligence and Computing, Tianjin University（智能与计算学院，天津大学）；State Key Lab. for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Yunnan Key Lab. of Service Computing, Yunnan University of Finance and Economics（云南服务计算重点实验室，云南财经大学）
Personalized federated prototype learning in mixed heterogeneous data scenarios
Guangxi Normal University（广西师范大学）；Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, GuangXi Normal University（教育区块链与智能技术重点实验室）；Hefei University of Technology（合肥工业大学）
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Statistics, Renmin University of China（中国人民大学统计学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University（虚拟现实技术与系统国家重点实验室，计算机科学与工程学院，北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo（人工智能商业创新中心，管理科学与系统系，布法罗大学）
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Center for Applied Statistics, School of Statistics, Renmin University of China（中国人民大学统计学院应用统计中心）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Department of Statistics, Fudan University（复旦大学统计系）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔蒂研究所，图宾根大学，德国）；Tübingen AI Center, University of Tübingen, Germany（图宾根人工智能中心，图宾根大学，德国）
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
Kyung Hee University（庆熙大学）；The University of Danang—Vietnam-Korea University of Information and Communication Technology（岘港大学—越韩信息与通信技术大学）；Digital Science and Technology Institute（数字科学技术研究院）
Rethinking Client Drift in Federated Learning: A Logit Perspective
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）；Wuhan University（武汉大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；Harbin Engineering University（哈尔滨工程大学）
Xidian University（西安电子科技大学）；JD Tech（京东科技）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；JD Finance America Corporation（京东金融美国公司）；Simon Fraser University（西蒙弗雷泽大学）；University of Pittsburgh（匹兹堡大学）
