Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity-Reference-Cited by-同舟云学术

Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity

Author:

Reisizadeh Amirhossein¹^ORCID,Tziotis Isidoros²,Hassani Hamed³^ORCID,Mokhtari Aryan²^ORCID,Pedarsani Ramtin⁴^ORCID

Affiliation:

1. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA

2. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

3. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

4. Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

Reference41 articles.

1. Distributed learning with compressed gradient differences;mishchenko;arXiv 1901 09269,2019

2. Competing with the empirical risk minimizer in a single pass;frostig;Proc Conf Learn Theory,2015

3. A unified theory of decentralized SGD with changing topology and local updates;koloskova;arXiv 2003 10422,2020

4. Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD;wang;arXiv 1810 08313,2018

5. The error-feedback framework: Better rates for SGD with delayed gradients and compressed communication;stich;arXiv 1909 05350,2019

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