Joint power cost and latency minimization for secure collaborative learning system

Abstract

This work investigates the update security model in a collaborative learning or federated learning network by using the covert communication. The covert communication (CC) uses the jamming signal and multiple friendly jammers (FJs) are deployed that can offer jamming services to the model owner, i.c., a base station (BS). To enable the BS to select the best FJ, i.e., the lowest cost FJ, a truthful auction is adopted. Then, we formulate an optimization problem that aims to optimize the transmission power, jamming power, and local accuracy. The objective is to minimize the training latency, subject to the security performance requirement and budget of the BS. To solve the non-convex problem, we adopt a Successive Convex Approximation algorithm. The numerical results reveal some interesting things. For example, the trustful auction reduces the jamming cost of the BS as the number of FJs increases.