Scheduling and Resource Allocation for Federated Learning in Vehicular Networks

In federated learning (FL), clients update their local machine learning models using private data that is not to be shared with others. In each update period, the local models are then shared with a central server that maintains a global model that is used by all the clients. In this paper we consider the problem of scheduling and bandwidth assignment for vehicles that share a wireless communication channel during the FL. The objective is to minimize the update period duration so that global model updates can occur as quickly as possible. This is done by creating a transmission schedule and a fractional bandwidth assignment for each FL update period. The problem is modeled as a mixed-integer nonlinear program (MINLP) and since the problem is NP-complete, approximation algorithms are introduced that yield near-optimal solutions. This is done by doing a binary search on the update duration using a fractional relaxation and then by applying different dependent rounding procedures to obtain valid solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions when compared to the results obtained by an optimum direct solver on the same inputs.