FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
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Abstract (arXiv)
In the Industrial Internet of Things (IoT), a large amount of data will be generated every day. Due to privacy and security issues, it is difficult to collect all these data together to train deep learning models, thus the federated learning, a distributed machine learning paradigm that protects data privacy, has been widely used in IoT. However, in practical federated learning, the data distributions usually have large differences across devices, and the heterogeneity of data will deteriorate the performance of the model. Moreover, federated learning in IoT usually has a large number of devices involved in training, and the limited communication resource of cloud servers become a bottleneck for training. To address the above issues, in this paper, we combine centralized federated learning with decentralized federated learning to design a semi-decentralized cloud-edge-device hierarchical federated learning framework, which can mitigate the impact of data heterogeneity, and can be deployed at lage scale in IoT. To address the effect of data heterogeneity, we use an incremental subgradient optimization algorithm in each ring cluster to improve the generalization ability of the ring cluster models. Our extensive experiments show that our approach can effectively mitigate the impact of data heterogeneity and alleviate the communication bottleneck in cloud servers.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 19 Mar 2024.
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18 of 31 references.
References · 18
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6 more
- 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
- 2020Federated Learning via Synthetic Datacited by 53
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- 2020TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecturecited by 12
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