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.

翻译：联邦学习（FL）使得边缘设备能够利用本地数据进行模型训练，同时保护数据隐私。然而，设备数据的异构性会导致学习精度下降，而针对计算能力与无线资源受限的设备更新大规模学习模型时，计算与通信延迟会显著增加。为应对这些挑战，我们提出了一种结合部分模型剪枝与个性化机制的联邦学习新框架。该框架将学习模型划分为两部分：通过模型剪枝与所有设备共享的全局部分，用于学习数据表征；以及针对特定设备微调的个性化部分，该机制在联邦学习过程中自适应调整模型大小，以降低计算与通信开销并最小化整体训练时间，同时提升非独立同分布（non-IID）数据设备的学习精度。随后，本文对所提联邦学习框架的计算与通信延迟及收敛性进行了数学分析。基于收敛性分析，我们构建了一个优化问题，通过联合优化剪枝比率与无线资源分配，在延迟阈值约束下最大化收敛速率。通过解耦优化问题并应用Karush-Kuhn-Tucker（KKT）条件，推导出剪枝比率与无线资源分配的闭式解。最终，实验结果表明，与仅采用模型个性化机制的方案相比，所提联邦学习框架在计算与通信延迟上实现了约50%的显著降低。