GT-NSGDm achieves the optimal non-asymptotic convergence rate O(1/T^{(p-1)/(3p-2)}) for decentralized nonconvex stochastic optimization under zero-mean heavy-tailed noise with p-th moment.
Efficient distributed optimization under heavy-tailed noise
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Derives finite-round upper-tail guarantee on population-empirical gap for client-sampled orthogonalized matrix momentum under heterogeneous data, with Lipschitz condition on the orthogonalizer.
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Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence
GT-NSGDm achieves the optimal non-asymptotic convergence rate O(1/T^{(p-1)/(3p-2)}) for decentralized nonconvex stochastic optimization under zero-mean heavy-tailed noise with p-th moment.