CANITA: Faster rates for distributed convex optimization with communication compression
Due to the high communication cost in distributed and federated learning, methods relying on compressed communication are becoming increasingly popular. Besides, the best theoretically and practically performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce th...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/8684 https://ink.library.smu.edu.sg/context/sis_research/article/9687/viewcontent/NeurIPS21_full_canita.pdf |
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| Institution: | Singapore Management University |
| Language: | English |