Abstract
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], Konecny 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.
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Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H. (2020). Distributed Machine Learning. In: Federated Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-01585-4_3
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DOI: https://doi.org/10.1007/978-3-031-01585-4_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00457-5
Online ISBN: 978-3-031-01585-4
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