As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiq...As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale networks.However,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns.Federated learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G.In this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.We then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.展开更多
基金supported by the National Research Foundation(NRF),Singapore,under Singapore Energy Market Authority(EMA),Energy Resilience,NRF2017EWT-EP003-041,Singapore NRF2015NRF-ISF001-2277Singapore NRF National Satellite of Excellence,Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007+4 种基金A*STARNTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing RGANS1906,Wallenberg AI,Autonomous Systems and Software Program and Nanyang Technological University(WASP/NTU)under grant M4082187(4080),and NTU-We Bank JRI(NWJ-2020-004)Alibaba Group through Alibaba Innovative Research(AIR)Program and Alibaba-NTU Singapore Joint Research Institute(JRI),NTU,SingaporeNational Key Research and Development Program of China under Grant 2018YFC0809803 and Grant 2019YFB2101901Young Innovation Talents Project in Higher Education of Guangdong Province,China under grant No.2018KQNCX333in part by the National Science Foundation of China under Grant 61702364。
文摘As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale networks.However,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns.Federated learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G.In this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.We then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.
