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Federated Zeroth-Order Optimization with Direction Aggregation and Variance Reduction
We study constrained nonsmooth nonconvex stochastic optimization in federated settings, where clients access only stochastic function evaluations. Existing federated zeroth-order methods primarily combine local zeroth-order updates with model averaging. However, they struggle with client drift induc
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