Federated Learning for Sparse Principal Component Analysis

Sin Cheng Ciou*, Pin Jui Chen, Elvin Y. Tseng, Yuh Jye Lee

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

In the rapidly evolving realm of machine learning, algorithm effectiveness often faces limitations due to data quality and availability. Traditional approaches grapple with data sharing due to legal and privacy concerns. The federated learning framework addresses this challenge. Federated learning is a decentralized approach where model training occurs on client sides, preserving privacy by keeping data localized. Instead of sending raw data to a central server, only model updates are exchanged, enhancing data security. We apply this framework to Sparse Principal Component Analysis (SPCA) in this work. SPCA aims to attain sparse component loadings while maximizing data variance for improved interpretability. Beside the l1 norm regularization term in conventional SPCA, we add a smoothing function to facilitate gradient-based optimization methods. Moreover, in order to improve computational efficiency, we introduce a least squares approximation to original SPCA. This enables analytic solutions on the optimization processes, leading to substantial computational improvements. Within the federated framework, we formulate SPCA as a consensus optimization problem, which can be solved using the Alternating Direction Method of Multipliers (ADMM). Our extensive experiments involve both IID and non-IID random features across various data owners. Results on synthetic and public datasets affirm the efficacy of our federated SPCA approach.

原文English
主出版物標題Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
編輯Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1081-1086
頁數6
ISBN(電子)9798350324457
DOIs
出版狀態Published - 2023
事件2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy
持續時間: 15 12月 202318 12月 2023

出版系列

名字Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference2023 IEEE International Conference on Big Data, BigData 2023
國家/地區Italy
城市Sorrento
期間15/12/2318/12/23

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