  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.
