论文标题
渠道不确定性下的联合学习：联合客户调度和资源分配
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
论文作者
论文摘要
在这项工作中，我们提出了一种新颖的联合客户计划和资源块（RB）分配策略，以最大程度地减少与基于集中培训的解决方案相比，在不完美的渠道状态信息（CSI）下，无线学习的准确性（FL）比无线的丧失。首先，该问题是在预定义的训练持续时间内将其作为随机优化问题施放，并使用Lyapunov优化框架解决。为了学习和跟踪无线通道，将高斯流程回归（GPR）的通道预测方法杠杆化并将其纳入调度决定。在完美和不完美的CSI下，通过数值模拟评估了提出的调度策略。结果表明，与最先进的客户调度和RB分配方法相比，提出的方法将准确性损失降低了25.8％。
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI). First, the problem is cast as a stochastic optimization problem over a predefined training duration and solved using the Lyapunov optimization framework. In order to learn and track the wireless channel, a Gaussian process regression (GPR)-based channel prediction method is leveraged and incorporated into the scheduling decision. The proposed scheduling policies are evaluated via numerical simulations, under both perfect and imperfect CSI. Results show that the proposed method reduces the loss of accuracy up to 25.8% compared to state-of-the-art client scheduling and RB allocation methods.
