Abstract
We propose an adaptive quantization method for two important distributed computation tasks: federated learning and distributed optimization. In both settings, we propose adaptive bit allocation schemes that allow nodes to trade their bandwidth with a minimal communication overhead. We show that the proposed schemes lead to an improvement in the speed of convergence of these methods compared to a uniform bit allocation method, especially when the data distribution among the nodes is skewed. Our theoretical results are corroborated by extensive simulations on various datasets.
Original language | English (US) |
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Title of host publication | 60th IEEE Conference on Decision and Control, CDC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1994-2001 |
Number of pages | 8 |
ISBN (Electronic) | 9781665436595 |
DOIs | |
State | Published - 2021 |
Event | 60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States Duration: Dec 13 2021 → Dec 17 2021 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2021-December |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 60th IEEE Conference on Decision and Control, CDC 2021 |
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Country/Territory | United States |
City | Austin |
Period | 12/13/21 → 12/17/21 |
Bibliographical note
Funding Information:The work of H. Reisizadeh and S. Mohajer is supported in part by the National Science Foundation under Grants CCF-1617884.
Publisher Copyright:
© 2021 IEEE.