Preface to Federated Learning: Algorithms, Systems, and Applications: Part 2

Abstract

We are delighted to present this special issue on Federated Learning: Algorithms, Systems, and Applications. Federated learning (FL) enables us to collaboratively learn a shared learning framework while distributing the data to clients instead of centralized storage. It allows for governments and businesses to design lower-latency and less-power-consumingmodels while ensuring data privacy, which is crucial for the development of systems and applications such as healthcare systems, the Internet of Vehicles (IoV), and smart cities. Since stricter regulations on privacy and security exacerbate the data fragmentation and isolation problem, where data holders are unwilling or prohibited to share their raw data freely, emerging frameworks based on federated learning are required to solve the above problems. The purpose of this special issue is to provide a forum for researchers and practitioners to present their latest research findings and engineering experiences in the theoretical foundations, empirical studies, and novel applications of federated learning for next-generation intelligent systems. This special issue consists of two parts. In Part 2, the guest editors selected 11 contributions that cover varying topics within this theme, ranging from privacy-aware IoV service deployment with federated learning in cloud-edge computing to federated multi-task graph learning. From this part, you can get the latest progress of federated learning, which may provide a new direction for your research. You can also learn the basic ideas and methods of federated learning and find inspiration from their research ideas on problems. In addition, the frame structure and diagram configuration of their articles may provide a template for your relevant papers. Xu et al., in “PSDF: Privacy-Aware IoV Service Deployment with Federated Learning in CloudEdge Computing,” propose a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing that copes with the dynamical service deployment problem for IoV in cloud-edge computing while protecting the privacy of edge servers. Zhong et al., in “FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge and End Device,” comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework, FLEE, which can realize dynamical updates of models without redeploying them. Dang et al., in “Federated Learning for Electronic Health Records,” survey existing works on FL applications in electronic health records (EHRs) and evaluate the performance of current state-ofthe-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real-world multi-center EHR dataset.