关键词: 联邦学习, 差分隐私, 交通流量预测, GRU, 隐私保护, 数据安全

Abstract: Urban traffic flow prediction is becoming more and more important in traffic management. However, these data often belong to different institutions and cannot be interconnected, and these data involve the privacy of the traveling public, so there are risks in centralized storage. Some researchers have used federated learning model to predict traffic, but federated learning itself also has privacy risks. This paper proposes a federated learning method for privacy protection of traffic flow prediction, which uses the federated learning algorithm based on flow prediction algorithm GRU with privacy protection ability to predict traffic flow. The specific approach is to introduce differential privacy into the local GRU algorithm, make the DPGRU algorithm on the client meet the requirement of (ε,δ)differential privacy by adding Gaussian noise to the gradient and make model parameters random. This paper analyzes the privacy of DPGRU algorithm on the client, and makes comparative experiments on the actual traffic flow data set. Experiments show that the method obtains better prediction results on the premise of ensuring privacy.

Key words: federated learning, differential privacy, traffic flow prediction, GRU, privacypreserving, data security