A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He

Research output: Contribution to journalArticlepeer-review

196 Citations (Scopus)


As data privacy increasingly becomes a critical societal concern, federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on FLSs. To understand the key design system components and guide future research, we introduce the definition of FLSs and analyze the system components. Moreover, we provide a thorough categorization for FLSs according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of FLSs as shown in our case studies. By systematically summarizing the existing FLSs, we present the design factors, case studies, and future research opportunities.

Original languageEnglish
Pages (from-to)3347-3366
Number of pages20
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number4
Early online date2 Nov 2021
Publication statusPublished - 1 Apr 2023


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