打破数据孤岛：联邦学习近期重要研究进展(41)_机器之心原创作者：仵冀颖编辑：H4O随

[1] Tian Li, et al.「Federated Learning: Challenges, Methods, and Future Directions,」https://arxiv.org/abs/1908.07873, 2019.

[2] H. B. McMahan, et al.「Communication-efficient learning of deep networks from decentralized data,」in Proc. of the International Conference on Artificial Intelligence and Statistics, 2017.

[3] J. Konecn′y, et al.「Federated learning: Strategies for improving communication efficiency,」NIPS 2016.

[4] Thibaux, R. and Jordan, M. I.「Hierarchical Beta processes and the Indian buffet process,」In Artificial Intelligence and Statistics , pp. 564–571, 2007.

[5] T. Lee. Tensornets. https://github.com/taehoonlee/tensornets, 2018.

作者介绍：仵冀颖，工学博士，毕业于北京交通大学，曾分别于香港中文大学和香港科技大学担任助理研究员和研究助理，现从事电子政务领域信息化新技术研究工作。主要研究方向为模式识别、计算机视觉，爱好科研，希望能保持学习、不断进步。