https://scholars.lib.ntu.edu.tw/handle/123456789/581434
標題: | Performance Analysis and Optimization for Federated Learning Applications with PySyft-based Secure Aggregation | 作者: | Lin P.-S Kao M.-C Liang W.-Y SHIH-HAO HUNG |
關鍵字: | Data Privacy; Federated Learning; Performance Modeling; Secure Aggregation | 公開日期: | 2020 | 起(迄)頁: | 191-196 | 來源出版物: | Proceedings - 2020 International Computer Symposium, ICS 2020 | 摘要: | To address privacy concerns, federated learning (FL) is becoming a promising machine learning technique which enables multiple decentralized clients to train a shared model collaboratively while preserving their private training data. Although FL may reduce the risks of data leak, it is still possible for hackers to reverse-engineer a trained model and figure out the information in the original training dataset provided by a FL client. In order to avoid such risks, secure aggregation (SA) can be used to privately combine the trained models of the clients to update the shared model. However, SA usually introduces performance overhead as it requires additional computation for encryption operations and even communications when secure multi-party computation (SMPC) is used. In this paper, we analyze the performance of FL with SA using PySyft, an open source framework including FL implementation, and propose an asynchronous FL mechanism to improve the overall performance. It turns out that the performance depends on the computational capabilities of the clients and the characteristics of the communication network, and we propose a performance modeling method to help system designers break down the execution time and decide on suitable trade-offs between privacy, efficiency, and accuracy for a balanced system. ? 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102199791&doi=10.1109%2fICS51289.2020.00046&partnerID=40&md5=aaafa86ee565994c45079c867dcbd471 https://scholars.lib.ntu.edu.tw/handle/123456789/581434 |
DOI: | 10.1109/ICS51289.2020.00046 | SDG/關鍵字: | Cryptography; Economic and social effects; Personal computing; Personnel training; Privacy by design; Computational capability; Encryption operations; Machine learning techniques; Open source frameworks; Performance analysis and optimizations; Performance Model; Secure aggregations; Secure multi-party computation; Learning systems |
顯示於: | 資訊工程學系 |
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