Biased-DMT achieves linear speedup and improved convergence in nonconvex decentralized stochastic optimization despite biased gradients, eliminating structural heterogeneity error for absolute bias cases.
Linear conver- gence of first- and zeroth-order primal-dual algorithms for distributed nonconvex optimization,
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Improved Convergence for Decentralized Stochastic Optimization with Biased Gradients
Biased-DMT achieves linear speedup and improved convergence in nonconvex decentralized stochastic optimization despite biased gradients, eliminating structural heterogeneity error for absolute bias cases.