The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation. However, the unbalanced local data distributions (either in quantity or quality) of participating clients, contributing non-equivalently to the global model training, still pose a big challenge to these works. In this paper, we propose FedCliP, a novel communication efficient FL framework that allows faster model training, by adaptively learning which clients should remain active for further model training and pruning those who should be inactive with less potential contributions. We also introduce an alternative optimization method with a newly defined contribution score measure to facilitate active and inactive client determination. We empirically evaluate the communication efficiency of FL frameworks with extensive experiments on three benchmark datasets under both IID and non-IID settings. Numerical results demonstrate the outperformance of the porposed FedCliP framework over state-of-the-art FL frameworks, i.e., FedCliP can save 70% of communication overhead with only 0.2% accuracy loss on MNIST datasets, and save 50% and 15% of communication overheads with less than 1% accuracy loss on FMNIST and CIFAR-10 datasets, respectively.

翻译：当前主流的通信高效联邦学习框架通常采用模型梯度压缩或模型蒸馏技术。然而，参与客户端之间非平衡的本地数据分布（无论是数量还是质量）对全局模型训练的贡献存在差异，这给现有工作带来了巨大挑战。本文提出了FedCliP——一种新颖的通信高效联邦学习框架，通过自适应学习保留哪些客户端继续参与模型训练，并剪枝贡献潜力较低的客户端，从而加速模型训练。我们同时引入了一种基于新定义的贡献度度量的交替优化方法，用于判定活跃与非活跃客户端。通过在三个基准数据集上（独立同分布与非独立同分布场景下）开展广泛实验，我们实证评估了联邦学习框架的通信效率。数值结果表明，所提出的FedCliP框架性能优于现有最先进的联邦学习框架：在MNIST数据集上，FedCliP可节省70%的通信开销，仅损失0.2%的准确率；在FMNIST和CIFAR-10数据集上，分别可节省50%和15%的通信开销，且准确率损失低于1%。