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08/16/2023
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Communication compression, a technique aiming to reduce the information ...
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06/28/2023
Momentum Benefits Non-IID Federated Learning Simply and Provably
Federated learning is a powerful paradigm for large-scale machine learni...
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05/25/2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Communication compression is a common technique in distributed optimizat...
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05/12/2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
Communication compression is an essential strategy for alleviating commu...
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11/01/2022
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks
Decentralized optimization with time-varying networks is an emerging par...
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10/14/2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Decentralized optimization is effective to save communication in large-s...
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06/08/2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Recent advances in distributed optimization and learning have shown that...
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04/25/2021
On the Comparison between Cyclic Sampling and Random Reshuffling
When applying a stochastic/incremental algorithm, one must choose the or...
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04/24/2021
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
The scale of deep learning nowadays calls for efficient distributed trai...
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12/28/2019