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 induced by local projected updates and sampling variance in stochastic zeroth-order estimates. In this paper, we propose a federated zeroth-order framework based on direction aggregation, equipped with two local sampling schemes. Specifically, FedZOO constructs a local direction from a minibatch shared across multiple spherical queries, thereby controlling the stochastic errors arising from data sampling and gradient approximation. In contrast, FedVRZO forms its local estimator from independent sample--direction pairs, so that its sampling error is controlled directly by the number of sample--direction pairs. In both algorithms, clients compute the mean of the directions evaluated along their projected local trajectories, and the server performs a single projected update using the weighted aggregate of the client directions. Furthermore, we establish convergence guarantees for FedZOO and FedVRZO, respectively. Experiments on black-box adversarial attacks demonstrate the effectiveness of the proposed methods.