Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
This work addresses a specific bottleneck in federated learning for edge AI applications where gradients are inaccessible, offering a novel method but with incremental improvements over existing zeroth-order techniques.
The paper tackles the problem of federated learning in scenarios where gradient information is unavailable, such as black-box attacks and hyperparameter tuning, by proposing a derivative-free federated zeroth-order optimization (FedZO) algorithm that performs multiple local updates and allows partial device participation, with simulation results demonstrating its effectiveness.
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, ranging from first-order to second-order methods. However, these algorithms cannot be applied in scenarios where the gradient information is not available, e.g., federated black-box attack and federated hyperparameter tuning. To address this issue, in this paper we propose a derivative-free federated zeroth-order optimization (FedZO) algorithm featured by performing multiple local updates based on stochastic gradient estimators in each communication round and enabling partial device participation. Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence. To enable communication-efficient FedZO over wireless networks, we further propose an over-the-air computation (AirComp) assisted FedZO algorithm. With an appropriate transceiver design, we show that the convergence of AirComp-assisted FedZO can still be preserved under certain signal-to-noise ratio conditions. Simulation results demonstrate the effectiveness of the FedZO algorithm and validate the theoretical observations.