Secure Federated Learning with Efficient Communication in Vehicle Network,ERICDATA高等教育知識庫
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篇名
Secure Federated Learning with Efficient Communication in Vehicle Network
並列篇名
Secure Federated Learning with Efficient Communication in Vehicle Network
作者 Yinglong LiZhenjiang ZhangZhiyuan ZhangYi-Chih Kao
英文摘要
Internet of Vehicles (IoV) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of traffic events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoV data to the edge server for centralized model training. Federated learning is a promising paradigm for distributed machine learning, which enables edge nodes to train models locally. As vehicle usually has unreliable and relatively slow network connection, reducing the communication overhead is importance. In this paper, we propose a secure federated learning with efficient communication (SFLEC) scheme in vehicle network. To protect the privacy of local update, we upload the updated parameters of the model with local differential privacy. We further propose a client selection approach that identifies relevant updates trained by vehicles and prevents irrelevant updates from being uploaded for reduced network footprint to achieve efficient communication. Then we prove the loss function of the trained FL in our scheme exits a theoretical convergence. Finally, we evaluate our scheme on two datasets and compare with basic FL. Our proposed scheme improves the communication efficiency, while preserves the data privacy.
起訖頁 2075-2084
關鍵詞 Edge computingFederated learningPrivacy preservationClient selection
刊名 網際網路技術學刊  
期數 202012 (21:7期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642020122107022  複製DOI
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