Distributed Machine Learning

As we know from Chapter 1, federated learning and distributed machine learning (DML) share several common features, e.g., both employing decentralized datasets and distributed training. Federated learning is even regarded as a special type of DML by some researchers, see, e.g., Phong and Phuong [2019], Yu et al. [2018], Konecný et al. [2016b] and Li et al. [2019], or seen as the future and the next step of DML. In order to gain deeper insights into federated learning, in this chapter, we provide an overview of DML, covering both the scalability-motivated and the privacy-motivated paradigms.

DML covers many aspects, including distributed storage of training data, distributed operation of computing tasks, ...

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