Journal
IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
Volume 1, Issue -, Pages 35-44Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OJCS.2020.2993259
Keywords
Edge computing; federated learning; internet of things; personalization
Categories
Funding
- National Key Research and Development Program of China [2017YFB1001703]
- National Science Foundation of China [61972432]
- Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X355]
- Pearl River Talent Recruitment Program [2017GC010465]
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Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this paper, we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneities in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.
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