Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.

Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems (ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods show their limitation in coping with complexity and high nonlinearity of traffic data as well as learning spatial-temporal dependencies. Particularly, they often neglect the dynamics happening to traffic network. Attention-based models witnessed extensive developments in recent years and have shown its efficacy in a host of fields, which inspires us to leverage graph-attention-based method to handling traffic network speed prediction. In this paper, we propose a novel deep learning framework, Spatial-Temporal Graph Attention Networks (ST-GAT). A graph attention mechanism is adopted to extract the spatial dependencies among road segments. Additionally, we introduce a LSTM network to extract temporal domain features. Compared with previous related research, the proposed approach is able to capture dynamic spatial dependencies of traffic networks. A series of comprehensive case studies on a real-world dataset demonstrate that ST-GAT supersedes existing state-of-the-art results of traffic speed prediction. Furthermore, outstanding robustness against noise and on reduced graphs of the proposed model has been demonstrated through the tests.

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