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Open Access

Federated Learning on Heterogeneous Images in Internet of Biomedical Things

College of Information Science and Technology, Jinan University, Guangzhou 510632, China
Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou 215316, China
College of Computer, Qinghai Normal University, Qinghai 810000, China
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Abstract

Federated Learning (FL) allows the Internet of bioMedical Things (IoMT) devices to collaboratively train a global model without centralizing data, yet the heterogeneity of biomedical images poses challenges in achieving optimal performance for each IoMT device. Although many personalized FL methods have been proposed, their adaptability is often limited to data with certain levels of heterogeneity. In this paper, we propose a novel FL method to handle heterogeneous images in IoMT. First, we propose a gradient-fusion method that integrates both shared and local gradients into the locally adapted model for improved personalization. The shared gradients, aggregated from all IoMT devices through a learnable matrix, capture collective intelligence, while the local gradients, originating from each device’s data, reflect individual data distributions. This dynamic integration of collective and device-specific insights effectively mitigates data heterogeneity. Second, we propose a privacy-enhanced approach that delegates part of the gradient computation to devices, thereby protecting data privacy without compromising the efficacy of the gradient-fusion process. Finally, for enhanced performance, we introduce a layer-wise aggregation method to precisely measure the contribution of different layers in local models. Extensive evaluations on imaging datasets, featuring various types and degrees of data heterogeneity, demonstrate the superior performance of our methods over existing baselines.

Keywords

Internet of bioMedical Things (IoMT)Federated Learning (KL)data heterogeneitygradient-fusionprivacy

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Big Data Mining and Analytics
Volume 7 Issue 4,
December 2024
Pages 1237-1250
DOI: 10.26599/BDMA.2024.9020067
Cite this article:
Li Y, He Z, He P, et al. Federated Learning on Heterogeneous Images in Internet of Biomedical Things. Big Data Mining and Analytics, 2024, 7(4): 1237-1250. https://doi.org/10.26599/BDMA.2024.9020067

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Received: 19 February 2024
Revised: 27 June 2024
Accepted: 30 September 2024
Published: 04 December 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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