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Uniform weighting in decentralized optimization can yield a vanishing tracking error, but discounted strategies may trap you in a persistent bias floor.
The resource requirements for decentralized gradient descent reveal surprising trade-offs that could redefine how we approach distributed optimization in complex networks.
