Chi Cmu, Haoyu Zhao, Li, Boyue Princeton, Zhize Cmu, Peter Cmu, Richtárik, Kaust Yuejie
We lifted 4 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| liboyue/beer | canonical | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| flatten_tensors | Ran | liboyue/beer/beer/optim/BEER.py pointer only (licence: NONE) · get_code("b4de8359d066f1a8") |
| BEER | Not yet run | liboyue/beer/beer/optim/BEER.py pointer only (licence: NONE) · get_code("960cf8d02ec51e18") |
| CompressedOptimizer | Not yet run | liboyue/beer/beer/optim/BEER.py pointer only (licence: NONE) · get_code("6a07bfbcba5cc212") |
| reduce_tensors | Not yet run | liboyue/beer/beer/optim/BEER.py pointer only (licence: NONE) · get_code("888aadb499a36597") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence O((G/T ) 2/3 ) compared with their uncompressed counterpart, where G measures the data heterogeneity across different clients, and T is the number of communication rounds. This paper proposes BEER, which adopts communication compression with gradient tracking, and shows it converges at a faster rate of O(1/T ). This significantly improves over the state-of-the-art rate, by matching the rate without compression even under arbitrary data heterogeneity. Numerical experiments are also provided to corroborate our theory and confirm the practical superiority of BEER in the data heterogeneous regime.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.13320")
get_code_for_paper("2201.13320")
have("2201.13320")
Connect an agent — have() is free.