A Survey on federated learning*

Federated learning (FL) is an emerging setting which implement machine learning in a distributed environment while protecting privacy. Research activities relating to FLhave grown at a fast rate recently in control. Exactly what activities have been carrying the research momentum forward is a question of interest to the research community. This study finds these research activities and optimization path of FL based on survey. Thus, this study aims to review related studies of FL to base on the baseline a universal definition gives a guiding for the future work. Besides, this study presents the prevailing FL applications and the evolution of federated learning. In the end, this study also identifies four research fronts to enrich the FL literature and help advance our understanding of the field. A comprehensive taxonomy of FL can also be developed through analyzing the results of this review.

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[49]  Mehryar Mohri,et al.  Agnostic Federated Learning , 2019, ICML.

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[54]  Sébastien Gambs,et al.  IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning , 2019, 2019 IEEE Security and Privacy Workshops (SPW).

[55]  Ameet Talwalkar,et al.  Federated Multi-Task Learning , 2017, NIPS.

[56]  Yaochu Jin,et al.  Multi-Objective Evolutionary Federated Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

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[60]  Kenneth T. Co,et al.  Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging , 2019, ArXiv.

[61]  Yasaman Khazaeni,et al.  Bayesian Nonparametric Federated Learning of Neural Networks , 2019, ICML.

[62]  Yue Zhao,et al.  Federated Learning with Non-IID Data , 2018, ArXiv.

[63]  Zhu Han,et al.  Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism , 2019, IEEE Communications Magazine.

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[65]  Yang Liu,et al.  Secure Federated Transfer Learning , 2018, ArXiv.

[66]  Tianjian Chen,et al.  HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography , 2019, ArXiv.