Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: Part 1

Abstract

We are delighted to present this special issue on Federated Learning: Algorithms, Systems, and Applications. Federated Learning enables 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 consumption models while ensuring data privacy, which is crucial for the development of systems and applications such as healthcare systems, the Internet of Vehicles, and smart city. 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 1, the guest editors selected 16 contributions that cover varying topics within this theme, ranging from algorithm-cryptographic co-designed deep neural network to personalized humor recognition. Zhou et al. in “Towards Scalable and Privacy-preserving Deep Neural Network via Algorithmiccryptographic Co-design” proposed a scalable and privacy-preserving deep learning network learning framework from algorithm-cryptographic co-perspective, which improves the computation ability and privacy. Antunes et al. in “Federated Learning for Healthcare: Systematic Review and Architecture Proposal” presented a systematic literature review on current research about federated learning (FL) in the context of electronic health records data for healthcare applications. They highlighted some proposed solutions and respective machine learning methods and discussed a general architecture for FL in healthcare. Liu et al. in “Federated Social Recommendation with Graph Neural Network” proposed a novel framework federated social recommendation with graph neural network for the social recommendation task, which is capable of handling heterogeneity and protecting the user’s privacy in social recommendation. Jiang et al. in “Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance” introduced Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences, which adopts the advantage of the graph neural network and is trained in a federated learning manner.