Advancements And Challenges In Federated Learning: General Approaches And Methods

Abstract

Federated Learning (FL) has turned out to be the shifted thinking in distributed ML that helps to overcome difficulties in organization privacy, data safety, and computation power. The aim of this review paper is to present new developments in the FL algorithms and to address new approaches and implementation, along with the problem that arises from them. We divide the developments into several topics, namely, content management in multi-party edge systems, efficient training in the energy-constrained edge computation, and decentralized platforms with efficient inter-device communication. We discuss the advanced FL algorithms including; The FedCo for content management, energy-conscious D2D assisted FL models, and decentralized FL frameworks similar to the ConFederated Learning (CFL). Towards this end, the paper also discusses different direction in wireless network optimization that might include dynamic resource allocation, hybrid local-centralized training models as well as DRL-based frameworks for resource management. Issues of FL such as, communication efficiency, privacy, and energy limitations are discussed in detail. We also discuss some of the solutions that have been proposed to solve them highlighted include; the use of blockchain for privacy-preserving caching, differential privacy techniques and energy-efficient scheduling. Finally, the reuse also discusses some of the challenges that arise when it comes to scaling up FL and offers recommendations that can guide future research activities to improve the performance of the FL in different settings for IoT, UAV networks, and the 6G system. Through the integration of more than a hundred research papers, this review also offers a bird’s eye view of the state-of-the-art of federated learning, as well as a research agenda for the fast-growing area. This paper will help researchers and practitioners who are interested in dealing with the various issues associated with federated learning and how best they can apply it across different domains.