An Incentive Mechanism for Federated Learning Based on Multiple Resource
  Exchange
Ruonan Dong Hui Xu Han Zhang GuoPeng Zhang
Federated Learning (FL) is a distributed machine learning paradigm that
addresses privacy concerns in machine learning and still guarantees high test
accuracy. However, achieving the necessary accuracy by having all clients
participate in FL is impractical, given the constraints of client local
computing resource. In this paper, we introduce a multi-user collaborative
computing framework, categorizing users into two roles: model owners (MOs) and
data owner (DOs). Without resorting to monetary incentives, an MO can encourage
more DOs to join in FL by allowing the DOs to offload extra local computing
tasks to the MO for execution. This exchange of "data" for "computing
resources" streamlines the incentives for clients to engage more effectively in
FL. We formulate the interaction between MO and DOs as an optimization problem,
and the objective is to effectively utilize the communication and computing
resource of the MO and DOs to minimize the time to complete an FL task. The
proposed problem is a mixed integer nonlinear programming (MINLP) with high
computational complexity. We first decompose it into two distinct subproblems,
namely the client selection problem and the resource allocation problem to
segregate the integer variables from the continuous variables. Then, an
effective iterative algorithm is proposed to solve problem. Simulation results
demonstrate that the proposed collaborative computing framework can achieve an
accuracy of more than 95\% while minimizing the overall time to complete an FL
task.