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.

通过多用户协作计算框架，将联邦学习中的模型所有者和数据所有者分为两种角色，通过允许数据所有者将额外的本地计算任务转移到模型所有者来有效地促使其参与，从而实现高准确度且最小化联邦学习任务所需时间的目标