Federated learning (FL) enables distributed learning across edge devices while protecting 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. We consider a novel FL framework with partial model pruning and personalization to overcome these challenges. 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 latency 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. To maximize the convergence rate and guarantee learning accuracy, Karush Kuhn Tucker (KKT) conditions are deployed to jointly optimize the pruning ratio and bandwidth 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）能够在保护数据隐私的前提下实现跨边缘设备的分布式学习。然而，设备数据的异构性会导致学习精度下降，且当在计算能力与无线资源受限的设备上更新大规模学习模型时，计算与通信延迟会显著增加。为克服这些挑战，本文提出了一种结合部分模型剪枝与个性化的新型联邦学习框架。该框架将学习模型分为全局部分与个性化部分：全局部分通过模型剪枝与所有设备共享以学习数据表征，个性化部分则针对特定设备进行微调。该框架在联邦学习过程中自适应调整模型规模，从而降低计算与通信延迟，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。为最大化收敛速率并保证学习精度，采用Karush-Kuhn-Tucker（KKT）条件对剪枝比例与带宽分配进行联合优化。实验结果表明，与仅采用模型个性化的方案相比，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。