LoDAdaC unifies multiple local training, Adam-style adaptive gradients, and compressed communication in a decentralized framework, with claimed complexity bounds and empirical gains on image classification and language model tasks.
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LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication
LoDAdaC unifies multiple local training, Adam-style adaptive gradients, and compressed communication in a decentralized framework, with claimed complexity bounds and empirical gains on image classification and language model tasks.