Adaptive privacy-preserving federated learning

As an emerging training model, federated deep learning has been widely applied in many fields such as speech recognition, image classification and classification of peer-to-peer (P2P) Internet traffics. However, it also entails various security and privacy concerns. In the past years, many researchers have been carried out toward elaborating solutions to alleviate the above challenges via three underlying technologies, i.e., Secure Multi-Party Computation (SMC), Homomorphic Encryption (HE) and Differential Privacy (DP). Compared with SMC and HE, differential privacy is outstanding in terms of efficiency. However, due to the involvement of noise, DP always needs to make a trade-off between security and accuracy. i.e., achieving a strong security requirement has to sacrifice certain accuracy. To seek the optimal balance, we propose APFL, an Adaptive Privacy-preserving Federated Learning framework in this paper. Specifically, in the APFL, we calculate the contribution of each attribute class to the outputs with a layer-wise relevance propagation algorithm. By injecting adaptive noise to data attributes, our APFL significantly reduces the impact of noise on the final results. Moreover, we introduce the Randomized Privacy-preserving Adjustment Technology to further improve the prediction accuracy of the model. We present a formal security analysis to demonstrate the high privacy level of APFL. Besides, extensive experiments show the superior performance of APFL in terms of accuracy, computation and communication overhead.