167 citations · 199 across the 6 of their papers we have counts for
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stat.ML2019★ 13 cited
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization
Navjot Singh, Deepesh Data, Jemin George +1
In this paper, we propose and analyze SPARQ-SGD, which is an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models. Each node c…
cs.DC2019
Data Encoding for Byzantine-Resilient Distributed Optimization
Deepesh Data, Linqi Song, Suhas Diggavi
We study distributed optimization in the presence of Byzantine adversaries, where both data and computation are distributed among worker machines, of which may be corrupt.…
stat.ML2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Debraj Basu, Deepesh Data, Can Karakus +1
Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models. Recently, several approaches to mitigate this proble…