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Found 2 papers, 1 papers with code
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical.
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing the clients to perform multiple local gradient-type training steps prior to communication.
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