Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework

By Qiong Wu, Kaiwen He, Xu Chen. IEEE Open Journal of the Computer Society, 2020

348 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a99008c7f0628b880cee5fc109deebd6be32791b

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “In [64], it was found helpful to address the statistical heterogeneity by embedding the notion of personalization in FL to capture non-specific aspects.”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “preserving data security and privacy, which is particularly appealing to AIoT applications in the context of edge computing [197], [221].”
Lower Bounds and Optimal Algorithms for Personalized Federated Learning (2020, background): “However, the requirements of several FL applications [53, 25, 10] question the appropriateness of the objective (1) as a modeling paradigm for FL.”
Personalized Federated Learning using Hypernetworks (2021, background): “This is the case, for instance, in IoT applications that create large amounts of data at edge devices, or with medical data that cannot be shared due to privacy (Wu et al., 2020).”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “In practical IoT applications, each client may expect to design their own local model architecture in a unique manner owing to differences in individual requirements and hardware constraints [216].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Meanwhile, in order to solve issues caused by device, data, and model heterogeneity that can adversely affect the FL process, a new personalized FL solution is proposed in [119] for cloudedge-based healthcare.”
Towards Personalized Federated Learning (2021, background): “These are practical issues prevalent in complex edge computing-based FL environments, where there is significant variability in hardware capabilities in terms of computation, memory, power and network connectivity [94].”