Stochastic Resource Allocation in Quantum Key Distribution for Secure Federated Learning

Abstract

Federated learning (FL) is a distributed machine learning paradigm with a promising future, which can preserve data privacy while training the global model collaboratively. However, FL is still facing model confidentiality issues. Therefore, in this paper, we propose a quantum key distribution (QKD) based secure FL scheme to facilitate FL model encryption against network eavesdropping attacks. Specifically, we introduce a stochastic resource allocation scheme for QKD to support FL networks. In the network, remote FL workers are connected to the server to train an aggregated global model in a distributed manner. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is not uniform. The proposed scheme can allocate QKD resources (i.e., wavelengths) in a way that minimizes the total cost given the stochastic demand. We formulate the optimization problem for the proposed scheme as a stochastic programming model. Numerical results demonstrate that the proposed scheme can successfully achieve the cost-minimizing objective while satisfying all uncertain demands and other security constraints.