This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network (GCN) to alleviate the time-consuming training, higher communication costs, and data privacy risks of global GCNs as the amount of data increases. The federated community GCN (FCGCN) can achieve timely, accurate, and safe traffic state predictions in the era of big traffic data, which is critical for the efficient operation of intelligent transportation systems. The FCGCN prediction process has four steps: dividing the local subnetwork with community detection, local training based on the global parameters, uploading the local model parameters, and constructing a global model prediction based on the aggregated parameters. Numerical results on the PeMS04 and PeMS08 datasets show that the FCGCN outperforms four benchmark models, namely, the long short-term memory (LSTM), convolutional neural network (CNN), ChebNet, and graph attention network (GAT) models. The FCGCN prediction is closer to the real value, with nearly the same performance as the global model at a lower time cost, thus achieving accurate and secure short-term traffic flow predictions with three parameters: flow, speed, and occupancy.

Deep generative models have gained much attention given their ability to generate data for applications as varied as healthcare to financial technology to surveillance, and many more - the most popular models being generative adversarial networks (GANs) and variational auto-encoders (VAEs). Yet, as with all machine learning models, ever is the concern over security breaches and privacy leaks and deep generative models are no exception. In fact, these models have advanced so rapidly in recent years that work on their security is still in its infancy. In an attempt to audit the current and future threats against these models, and to provide a roadmap for defense preparations in the short term, we prepared this comprehensive and specialized survey on the security and privacy preservation of GANs and VAEs. Our focus is on the inner connection between attacks and model architectures and, more specifically, on five components of deep generative models: the training data, the latent code, the generators/decoders of GANs/VAEs, the discriminators/encoders of GANs/VAEs, and the generated data. For each model, component and attack, we review the current research progress and identify the key challenges. The paper concludes with a discussion of possible future attacks and research directions in the field.