Survey on challenges of federated learning in edge computing scenarios

Yutao Zhang; Rui Zhai; Yingqi Wang; Xingyu Wang

doi:10.1117/12.2629118

22 April 2022 Survey on challenges of federated learning in edge computing scenarios

Yutao Zhang, Rui Zhai, Yingqi Wang, Xingyu Wang

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740C (2022) https://doi.org/10.1117/12.2629118
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

More and more edge devices are linked with the network along with the digital transformation of society and the rapid development of the Internet of things. A large number of edge devices produce a large amount of data which challenges the IOT network with cloud computing as the core computing power. The edge computing can save network bandwidth and reduce delay as the extension and supplement of cloud computing on the one hand; on the other hand, the characteristics of multiple mobile devices based on edge computing make it very suitable to realize big data fusion with the Federated Learning framework, the privacy and security of users can be greatly improved by edge computing based on Federated Learning, the data silos can be broken and a more intelligent Internet of things can be achieved. This paper summarizes the common algorithms of Federated Learning based on edge computing, analyzes the existing challenges, and summarizes the corresponding algorithms.

Citation Download Citation

Yutao Zhang, Rui Zhai, Yingqi Wang, and Xingyu Wang "Survey on challenges of federated learning in edge computing scenarios", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740C (22 April 2022); https://doi.org/10.1117/12.2629118

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