Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due to frequent model updates, FEEL needs to be adapted to the limited communication bandwidth, scarce energy of edge devices, and the statistical heterogeneity of edge devices' data distributions. Therefore, a careful scheduling of a subset of devices for training and uploading models is necessary. In contrast to previous work in FEEL where the data aspects are under-explored, we consider data properties at the heart of the proposed scheduling algorithm. To this end, we propose a new scheduling scheme for non-independent and-identically-distributed (non-IID) and unbalanced datasets in FEEL. As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. In fact, the data collected by the devices depends on the local environment and usage pattern. Thus, the datasets vary in size and distributions among the devices. In the proposed algorithm, we consider both data and resource perspectives. In addition to minimizing the completion time of FEEL as well as the transmission energy of the participating devices, the algorithm prioritizes devices with rich and diverse datasets. We first define a general framework for the data-aware scheduling and the main axes and requirements for diversity evaluation. Then, we discuss diversity aspects and some exploitable techniques and metrics. Next, we formulate the problem and present our FEEL scheduling algorithm. Evaluations in different scenarios show that our proposed FEEL scheduling algorithm can help achieve high accuracy in few rounds with a reduced cost.
Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices
Dept. of Electrical and Electronics Engineering, Bilkent University（电子与电子工程系，比尔肯特大学）；Dept. of Electrical and Electronic Engineering, Imperial College London（电子与电子工程系，帝国理工学院伦敦分校）
Age Aware Scheduling for Differentially-Private Federated Learning
Institute of Communications Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学通信工程研究所）；Simula UiB（西挪威应用科技大学西蒙研究所）；Department of Communication Engineering, National Taipei University（国立台北大学通信工程学系）
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
School of Electronic and Information Engineering, Beijing Jiaotong University（电子信息工程学院，北京交通大学）；Beijing National Research Center for Information Science and Technology（北京信息科学国家研究中心）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；School of Computing, Macquarie University（计算学院，麦考瑞大学）；Department of Broadband Communication, Peng Cheng Laboratory（宽带通信部，鹏城实验室）；China Telecom, Sichuan Branch（中国电信四川分公司）
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Nanyang Technological University（南洋理工大学）；DSO National Laboratories（DSO国家实验室）；Alibaba Group（阿里巴巴集团）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
UM-SJTU Joint Institute, Shanghai Jiao Tong University（上海交通大学密西根学院）；Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University（上海交通大学合作媒体创新中心）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beihang University（北京航空航天大学）
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
Department of Computer Science University of the Western Cape（计算机科学系，西开普敦大学）；Department of Computer Science Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Shanghai University（上海大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（英联邦科学与工业研究组织）；Fudan University（复旦大学）；Xidian University（西安电子科技大学）
Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning
Department of Computer and Information Science（计算机与信息科学系）；Linköping University（林奈技术大学）；Department of Information Engineering（信息工程系）；University of Brescia（布雷西亚大学）；ZJU-UIUC Institute（浙大-伊利诺伊大学联合研究所）；Zhejiang University（浙江大学）
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）；China Academy of Information and Communications Technology（中国信息通信研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Zhejiang University（浙江大学）
