Towards efficient communications in federated learning: A contemporary survey |
| |
Institution: | 1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China;2. Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China;3. City University of Hong Kong, Hong Kong, China;4. City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China;5. AsiaInfo Technologies, Beijing, China;6. RISC-V International Open Source Laboratory, Shenzhen, Guangdong, China;1. Departement of Electronic, Badji Mokhtar University, Annaba 23000, Algeria;2. School of Computer Science, University of Galway, Galway H91 TK33, Ireland |
| |
Abstract: | In the traditional distributed machine learning scenario, the user’s private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|