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.

翻译：联邦学习是一种分布式机器学习范式，既能解决机器学习中的隐私问题，又能保证较高的测试精度。然而，由于客户端本地计算资源的限制，让所有客户端参与联邦学习以实现必要精度并不现实。本文提出一种多用户协作计算框架，将用户分为两类角色：模型所有者（MOs）和数据所有者（DOs）。在不依赖货币激励的情况下，模型所有者允许数据所有者将额外的本地计算任务卸载至其执行，从而激励更多数据所有者参与联邦学习。这种"数据"与"计算资源"的交换模式能有效简化客户参与联邦学习的激励机制。我们将模型所有者与数据所有者之间的交互建模为一个优化问题，目标是通过高效利用模型所有者和数据所有者的通信与计算资源，最小化完成联邦学习任务的时间。该问题属于计算复杂度较高的混合整数非线性规划（MINLP）。我们首先将其分解为客户端选择与资源分配两个子问题，以分离整数变量与连续变量；随后提出一种高效的迭代算法进行求解。仿真结果表明，所提出的协作计算框架在最小化联邦学习任务完成时间的同时，可实现超过95%的精度。