Q-RGT proposes a quantization scheme with manifold-landing bias, but its O(1/K) convergence proof relies on a false unbiasedness assumption.
Exact diffusion for distributed optimization and learning—part i: Algorithm development,
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Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
Q-RGT proposes a quantization scheme with manifold-landing bias, but its O(1/K) convergence proof relies on a false unbiasedness assumption.