Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

Ying-Hsun Lai, Shin-Yeh Chen, Wen-Chi Chou, Hua-Yang Hsu () and Han-Chieh Chao ()
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Ying-Hsun Lai: Department of Computer Science and Information Engineering, National Taitung University, Taitung 950309, Taiwan
Shin-Yeh Chen: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
Wen-Chi Chou: Taiwan Semiconductor Manufacturing Company, Hsinchu 300096, Taiwan
Hua-Yang Hsu: School of Electronic and Computer Engineering (SECE), Peking University Shenzhen Graduate School, Shenzhen 518055, China
Han-Chieh Chao: Department of Electrical Engineering, National Dong Hwa University, Hualien 974301, Taiwan

Future Internet, 2024, vol. 16, issue 3, 1-13

Abstract: Federated learning trains a neural network model using the client’s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client’s data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

Keywords: personalized federated learning; client drift; non-IID datasets; neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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