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Communication-Efficient Distributed Optimization Algorithms

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Federated Learning

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Abstract

In federated learning, the communication link connecting the edge parties with the central aggregator is sometimes bandwidth-limited and can have high network latency. Therefore, there is a critical need to design and deploy communication-efficient distributed training algorithms. In this chapter, we will review two orthogonal communication-efficient distributed stochastic gradient descent (SGD) methods: (1) local-update stochastic gradient descent (SGD), where clients make multiple local model updates that are periodically aggregated, and (2) gradient compression and sparsification methods to reduce the number of bits transmitted per update. In both these methods, there is a trade-off between the error convergence with respect to the number of iterations and the communication efficiency.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Gauri Joshi

  2. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Shiqiang Wang

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  1. Gauri Joshi
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  2. Shiqiang Wang
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Correspondence to Shiqiang Wang .

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Editors and Affiliations

  1. IBM Research – Almaden, San Jose, CA, USA

    Heiko Ludwig

  2. IBM Research -- Almaden, San Jose, CA, USA

    Nathalie Baracaldo

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Joshi, G., Wang, S. (2022). Communication-Efficient Distributed Optimization Algorithms. In: Ludwig, H., Baracaldo, N. (eds) Federated Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-96896-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-96896-0_6

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