Federated Learning provides new opportunities for training machine learning models while respecting data privacy. This technique is based on heterogeneous devices that work together to iteratively train a model while never sharing their own data. Given the synchronous nature of this training, the performance of Federated Learning systems is dictated by the slowest devices, also known as stragglers. In this paper, we investigate the problem of minimizing the duration of Federated Learning rounds by controlling how much data each device uses for training. We formulate this problem as a makespan minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with non-decreasing cost functions while respecting lower and upper limits of tasks per resource. Based on this formulation, we propose a polynomial-time algorithm named OLAR and prove that it provides optimal schedules. We evaluate OLAR in an extensive experimental evaluation using simulation that includes comparisons to other algorithms from the state of the art and new extensions to them. Our results indicate that OLAR provides optimal solutions with a small execution time. They also show that the presence of lower and upper limits of tasks per resource erase any benefits that suboptimal heuristics could provide in terms of algorithm execution time.
MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning
California State University, Dominguez Hills（加州州立大学多明戈斯山分校）；University of California, Irvine（加州大学欧文分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Arizona State University（亚利桑那州立大学）
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Hong Kong Polytechnic University（香港理工大学）；Queen's University Belfast（贝尔法斯特女王大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Key Laboratory of Trusted Data Circulation（上海可信数据流通关键实验室）
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；International School, Beijing University of Posts and Telecommunications（北京邮电大学国际学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
Singapore University of Technology and Design（新加坡科技设计大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）前沿人工智能研究中心）；Chongqing University（重庆大学）；Nanyang Technological University（南洋理工大学）
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；St. Jude Children’s Research Hospital（圣犹大儿童研究医院）
