@article{15820, author = {Y. Lu and X. Huang and Y. Dai and Sabita Maharjan and Yan Zhang}, title = {Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics}, abstract = {Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.}, year = {2020}, journal = {IEEE Transactions on Industrial Informatics}, volume = {16}, pages = {2134 - 2143}, month = {03/2020}, publisher = {IEEE}, issn = {Print ISSN: 1551-3203 Electronic ISSN: 1941-0050}, url = {https://ieeexplore.ieee.org/document/8843942/authors$\#$authors}, doi = {10.1109/TII.2019.2942179}, }