Bandwidth Allocation for Federated Learning With Wireless Providers and Cost Constraints

Federated learning (FL) trains a global learning model by using a central server to collaborate with multiple decentralized clients. In a wireless network, the data transmission latency between a client and the FL server is substantially affected by signal quality dynamics and bandwidth allocation. FL clients require synchronized communication at each round to update their models simultaneously, which makes bandwidth allocation methods for conventional wireless tasks infeasible to use. Existing bandwidth allocation studies for FL mainly focused on allocating bandwidth of one bandwidth provider without cost. In this paper, we consider a more practical and challenging problem: how to assign the bandwidth to clients under multiple wireless providers to minimize the FL round length (i.e., the latency that FL finishes one round of model training and updating) with bandwidth capability and cost constraints? We propose a model that maps the problem into a new variant of the knapsack problem, called multi-dimensional max-min multiple knapsacks (MDM$^{\,3}$KP). Based on MDM$^{\,3}$KP, we create an iterative solution to find the client assignment and bandwidth allocation that minimizes the FL round length. Comprehensive simulation results show that the solution reduces the FL round length by up to 70.8% compared with other benchmarks.