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Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is becoming more apparent.
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
We extend the stochastic extragradient method to this very general setting and theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone (when a Minty solution exists) settings.
On the Effect of Consensus in Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters.
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