Federated learning (FL) allows model training from local data by edge devices
while preserving data privacy. However, the learning accuracy decreases due to
the heterogeneity of devices data, and the computation and communication
latency increase when updating large scale learning models on devices with
limited computational capability and wireless resources. To overcome these
challenges, we consider a novel FL framework with partial model pruning and
personalization. This framework splits the learning model into a global part
with model pruning shared with all devices to learn data representations and a
personalized part to be fine tuned for a specific device, which adapts the
model size during FL to reduce both computation and communication overhead and
minimize the overall training time, and increases the learning accuracy for the
device with non independent and identically distributed (non IID) data. Then,
the computation and communication latency and the convergence analysis of the
proposed FL framework are mathematically analyzed. Based on the convergence
analysis, an optimization problem is formulated to maximize the convergence
rate under a latency threshold by jointly optimizing the pruning ratio and
wireless resource allocation. By decoupling the optimization problem and
deploying Karush Kuhn Tucker (KKT) conditions, we derive the closed form
solutions of pruning ratio and wireless resource allocation. Finally,
experimental results demonstrate that the proposed FL framework achieves a
remarkable reduction of approximately 50 percents computation and communication
latency compared with the scheme only with model personalization.

通过将学习模型分为全局部分和个性化部分，以实现模型剪枝和个性化，本文提出了一种解决异构设备数据、计算和通信延迟以及非独立同分布数据的联邦学习框架，并通过数学分析研究了其收敛性、计算和通信延迟，并最终通过优化问题得到了剪枝比例和无线资源分配的闭式解。实验结果表明，相比只进行模型个性化的方案，该框架能够显著减少约 50％的计算和通信延迟。