Privacy-Preserving and Low-Latency Federated Learning in Edge Computing

Abstract

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.