Journal of Applied Science and Engineering

Published by Tamkang University Press

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Chong Wang1, Jing Wang2, Zheng Lou1, Linghai Kong2, WeiSong Tao1, and Yun Wang2This email address is being protected from spambots. You need JavaScript enabled to view it.

1State Grid Jiangsu Electric Power Co. LTD, Nanjing, 210024, China

2School of computer science and technology, Southeast University, Nanjing, 211189, China

3Nanjing Yunchan Information Technology Co.,Nanjing, 211100, China


 

Received: July 14, 2024
Accepted: August 24, 2024
Publication Date: October 26, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202508_28(8).0003  


Cross-silo federated learning (FL) enables collaborative model training among various organizations (e.g., financial or medical). It operates by aggregating local gradient updates contributed by participating clients, all the while safeguarding the privacy of sensitive data. Industrial FL frameworks employ additively homomorphic encryption (HE) to ensure that local gradient updates are masked during aggregation, guaranteeing no update is revealed. However, this measure has resulted in significant computational and communication overhead. Encryption and decryption operations have occupied the majority of the training time. In addition, the bit length of ciphertext is two orders of magnitude larger than that of plaintext, inflating the data transfer amount. In this paper, we present a new gradient sparsification method, SparseBatch. By designing a new general gradient correction method and using Lion optimizer’s gradient quantization method, SparseBatch combines gradient sparsification and quantization. Experimental results show that compared with BatchCrypt, SparseBatch reduces the computation and communication overhead by 5×, and the accuracy reduction is less than 1.


Keywords: Homomorphic encryption, Federated Learning, Gradient sparsification, Gradient quantization, Lion optimizer


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