Jieren Cheng1, Zhenhao Liu1,*, Yiming Shi1, Ping Luo1,2, Victor S. Sheng3
CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758
Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of communication. When there are a… More >
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