Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework
Open accessAbstract
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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FL offers a general learning framework to use massive distributed and isolated data while preserving data security and privacy, which is particularly appealing to AIoT applications in the context of edge computing [221], [197].
“FL offers a general learning framework to use massive distributed and isolated data while preserving data security and privacy, which is particularly appealing to AIoT applications in the context of edge computing [221], [197].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [221]
The next generation of FL solution is cloud edge-based healthcare, which could address issues that directly affect the FL process, such as device, data, and model.
“The next generation of FL solution is presented in [74] cloud edge-based healthcare. This solution could be used to address issues that directly affect the FL process, such as device, data, and model.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [74]
FedBoost is a communication-efficient algorithm for FL based on ensemble learning.
“FedBoost [129] is a communication-efficient algorithm for FL based on ensemble learning technique.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [129]
The accuracy of PerFit is 11.12% higher than that of classical FL.
“The results show that the accuracy of PerFit is 11.12% higher than that of classical FL.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [45]
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- Authors
- Wu, Q., He, K., & Chen, X.
- Affiliations
- Sun Yat-sen University (CN)
- Venue
- IEEE Open Journal of the Computer Society
- Published
- 2020
- Volume
- 1
- Pages
- 35-44
- ISSN
- 2644-1268 (ISSN-L)
- OpenAlex
- W3021026170
- Cited by
- 424
- References
- 28
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 80 · 2023: 87 · 2022: 83 · 2021: 53 · 2020: 15
- FWCI
- 23.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Open Journal of the Computer Society (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Basic Research Program of China (2017YFB1001703)
- National Natural Science Foundation of China (61972432)
- Program for Guangdong Introducing Innovative and Enterpreneurial (2017ZT07X355)
- Pearl River Talent Recruitment Program (2017GC010465)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- personalized federated learning, Internet of Things, edge-cloud computing, edge computing, data heterogeneity, model heterogeneity, heterogeneous devices, human activity recognition, data privacy
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