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In compressed decentralized optimization settings, there are benefits to having multiple gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e.g. by means of reducing the precision of compressed information.

License: MIT License

Python 95.24% Shell 4.76%
federated-learning convex-optimization non-convex-optimization sgd sgd-optimizer machine-learning optimization-algorithms optimization optimization-methods distributed-systems distributed-computing high-performance-computing

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