Enhancing Federated Learning Performance on Heterogeneous IoT Devices Using Generative Artificial Intelligence With Resource Scheduling

The integration of Federated Learning (FL) with the Internet of Things (IoT) represents an advanced technological trend, combining the extensive connectivity of IoT with the powerful processing capabilities of FL to drive innovation and optimization across multiple domains. Given the heterogeneity o...

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Bibliographic Details
Published in:IEEE internet of things journal 2025, p.1-1
Main Authors: Meng, Zezhao, Li, Zhi, Hou, Xiangwang, Xu, Minrui, Xia, Yi, Zhang, Zekai, Song, Shaoyang
Format: Article
Language:English
Subjects:
Computational modeling
Data collection
Data models
Distributed databases
Federated learning
GAI
Generative AI
Horses
Internet of Things
IoT
Optimization
resource allocation
Training
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Summary:The integration of Federated Learning (FL) with the Internet of Things (IoT) represents an advanced technological trend, combining the extensive connectivity of IoT with the powerful processing capabilities of FL to drive innovation and optimization across multiple domains. Given the heterogeneity of IoT devices and the variability in data distribution, developing strategies to enhance FL performance without overly burdening resource-constrained devices is crucial. This paper proposes a FL algorithm based on Generative Artificial Intelligence (GAI) for IoT devices with extreme heterogeneity in data and resources. The algorithm utilizes pre-trained GAI models to generate new data, aligning the data distributions of individual IoT devices closer to independent and identically distributed (i.i.d.), thereby effectively reducing the heterogeneity of local data. Additionally, the proposed algorithm incorporates data synthesis and resource scheduling strategies to mitigate the heterogeneity of local device resources. Finally, we formulate a joint optimization problem aimed at minimizing total energy consumption while maximizing FL performance. Experimental results demonstrate that, under significant resource and data distribution disparities, most existing solutions struggle to converge, whereas the proposed method converges and achieves superior performance. Compared to existing GAI-based approaches, our method significantly reduces latency and energy consumption.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3521017