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05/27/2023
Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performan...
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10/28/2022
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
In this work, we study distributed optimization algorithms that reduce t...
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06/07/2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation
Despite their high computation and communication costs, Newton-type meth...
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11/02/2021
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Recent advances in distributed optimization have shown that Newton-type ...
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06/07/2021
Smoothness-Aware Quantization Techniques
Distributed machine learning has become an indispensable tool for traini...
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06/05/2021
FedNL: Making Newton-Type Methods Applicable to Federated Learning
Inspired by recent work of Islamov et al (2021), we propose a family of ...
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02/14/2021
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Large scale distributed optimization has become the default tool for the...
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10/07/2020
Optimal Gradient Compression for Distributed and Federated Learning
Communicating information, like gradient vectors, between computing node...
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02/27/2020
On Biased Compression for Distributed Learning
In the last few years, various communication compression techniques have...
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02/20/2020