Federated learning in mobile edge networks : a comprehensive survey

Abstract

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Research Foundation (NRF)Accepted versionThis research is supported, in part, by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWTEP003- 041, Singapore NRF2015-NRF-ISF001-2277, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeSTSCI2019- 0007, A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing RGANS1906, WASP/NTU M4082187 (4080), Singapore MOE Tier 2 MOE2014-T2-2-015 ARC4/15, MOE Tier 1 2017-T1-002-007 RG122/17, AI Singapore Programme AISG-GC-2019-003, NRF-NRFI05-2019-0002. This research is also supported, in part, by the Alibaba-NTU Singapore Joint Research Institute (Alibaba-NTU-AIR2019B1), Nanyang Technological University, Singapore. In addition, this research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.02-2019.305. The work of Y.-C. Liang was supported by the National Natural Science Foundation of China under Grants 61631005 and U1801261, the National Key R&D Program of China under Grant 2018YFB1801105, and the 111 Project under Grant B20064. Qiang Yang also thanks the support of Hong Kong CERG grants 16209715 and 16244616