In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models, while obeying the laws and regulations and ensuring data security and data privacy. In this paper, we provide a comprehensive survey of existing works for federated learning. We propose a functional architecture of federated learning systems and a taxonomy of related techniques. Furthermore, we present the distributed training, data communication, and security of FL systems. Finally, we analyze their limitations and propose future research directions.
@article{arxiv.2104.14362,
title = {From Distributed Machine Learning to Federated Learning: A Survey},
author = {Ji Liu and Jizhou Huang and Yang Zhou and Xuhong Li and Shilei Ji and Haoyi Xiong and Dejing Dou},
journal= {arXiv preprint arXiv:2104.14362},
year = {2022}
}