Privacy-Preserving Federated Deep Learning with Irregular Users
Guowen Xu, Hongwei Li, Yun Zhang, Shengmin Xu, Jianting Ning, Robert Huijie Deng · IEEE Transactions on Dependable and Secure Computing · 2020
Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preservingapproaches have been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (calledirregular users) may be of low quality. Obviously, in a federated training process, data shared by manyirregular usersmay impair the training accuracy, or worse, lead to the uselessness of the final model. In this article, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework withirregular users. In specific, we design a novel solution to reduce the negative impact ofirregular userson the training accuracy, which guarantees that the training results are mainly calculated from the contribution of high-quality data. Meanwhile, we exploit Yao's garbled circuits and additively homomorphic cryptosystems to ensure the confidentiality of all user-related information. Moreover, PPFDL is also robust to users dropping out during the whole implementation. This means that each user can be offline at any subprocess of training, as long as the remaining online users can still complete the training task. Extensive experiments demonstrate the superior performance of PPFDL in terms of training accuracy, computation, and communication overheads.