Adaptive and Communication-Efficient Zeroth-Order Optimization for Distributed Internet of Things

This article addresses the optimization problem of zeroth-order in a distributed setting, where the gradient information is not available in the edge Internet of Things (IoT) clients. The high communication costs and poorer convergence hinder the use of zeroth-order optimization methods in distributed IoT. This article proposes a communication-efficient Distributed adaptive Zeroth-order optimization method (DaZoo). DaZoo is applied to optimize a class of nonconvex optimization problems, where each client can only access zeroth-order information of local functions. To estimate the global gradient, each client uses a population-based feedback strategy to approximate the first-order gradient, which are then aggregated through a central server. A novel global adaptive optimization scheme is devised for DaZoo, making it with the flexibility to adapt to any landscape without the need for manual parameter tuning. Furthermore, sparsification techniques are incorporated into the local model differences to substantially reduce communication overhead. The theoretical findings suggest that DaZoo can reduce iteration complexity compared to the baselines. Case studies on distributed closed-box attacks and large-scale IoT attack detection demonstrate that DaZoo can outperform state-of-the-art methods.