Efficient federated knowledge distillation strategy for heterogeneous data in the Internet of Things

Huiqi Zhao; Qilin Xuan; Fang Fan; Huajie Zhang; Yaowen Ma

doi:10.1117/12.3031115

19 July 2024 Efficient federated knowledge distillation strategy for heterogeneous data in the Internet of Things

Huiqi Zhao, Qilin Xuan, Fang Fan, Huajie Zhang, Yaowen Ma

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131815N (2024) https://doi.org/10.1117/12.3031115
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the rapid evolution of Internet of Things (IoT) technology and the advent of the big data era, a vast network of interconnected devices and sensors has emerged. Traditional centralized model training methods are insufficient for meeting the data privacy and sharing requirements of IoT systems. Moreover, the inherent heterogeneity of data generated by these devices poses a significant security challenge. In response, this paper presents an innovative federated knowledge distillation strategy tailored for handling heterogeneous data in IoT environments. The proposed approach incorporates knowledge distillation into the federated learning framework, facilitating the transfer of knowledge from a teacher model to a more efficient student model. Communication efficiency is enhanced by transmitting predictive labels instead of the entire model parameters. To further narrow the gap between the teacher and student models and improve generalization, Kullback-Leibler and regular terms are integrated into the federated learning objective function. Additionally, we introduce a CNN-Transformer hybrid model on the client side, comprising a classifier and discriminator network. This architecture improves the quality of predicted labels, addressing training inefficiencies associated with highly heterogeneous data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huiqi Zhao, Qilin Xuan, Fang Fan, Huajie Zhang, and Yaowen Ma "Efficient federated knowledge distillation strategy for heterogeneous data in the Internet of Things", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131815N (19 July 2024); https://doi.org/10.1117/12.3031115

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KEYWORDS

Education and training

Machine learning

Internet of things

Data modeling

Transformers

Data communications

Performance modeling

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