Federated learning (FL) has emerged as a key technique for distributed
machine learning (ML). Most literature on FL has focused on systems with (i) ML
model training for a single task/model, (ii) a synchronous setting for
uplink/downlink transfer of model parameters, which is often unrealistic. To
address this, we develop MA-FL, which considers FL with multiple downstream
tasks to be trained over an asynchronous model transmission architecture. We
first characterize the convergence of ML model training under MA-FL via
introducing a family of scheduling tensors to capture the scheduling of
devices. Our convergence analysis sheds light on the impact of resource
allocation (e.g., the mini-batch size and number of gradient descent
iterations), device scheduling, and individual model states (i.e., warmed vs.
cold initialization) on the performance of ML models. We then formulate a
non-convex mixed integer optimization problem for jointly configuring the
resource allocation and device scheduling to strike an efficient trade-off
between energy consumption and ML performance, which is solved via successive
convex approximations. Through numerical simulations, we reveal the advantages
of MA-FL in terms of model performance and network resource savings.

本文提出了一种应用于多个下游任务的异步模型传输的联邦学习方法，通过引入调度张量来表征设备调度，并通过收敛性分析探究了资源分配、设备调度和个体模型状态等对机器学习性能的影响，进而通过非凸混合整数优化问题来调整资源分配和设备调度，以在能量消耗和性能之间达到高效平衡，并通过数值模拟揭示了该方法在模型性能和网络资源节约方面的优势。