Communication of model updates between client nodes and the central
aggregating server is a major bottleneck in federated learning, especially in
bandwidth-limited settings and high-dimensional models. Gradient quantization
is an effective way of reducing the number of bits required to communicate each
model update, albeit at the cost of having a higher error floor due to the
higher variance of the stochastic gradients. In this work, we propose an
adaptive quantization strategy called AdaQuantFL that aims to achieve
communication efficiency as well as a low error floor by changing the number of
quantization levels during the course of training. Experiments on training deep
neural networks show that our method can converge in much fewer communicated
bits as compared to fixed quantization level setups, with little or no impact
on training and test accuracy.

本文研究提出了 AdaQuant FL，一种自适应量化策略，旨在通过在训练过程中改变量化级别的数量来实现通信效率以及低误差率。 实验表明，与固定量化级别设置相比，该方法可以在更少的通信比特数中收敛，几乎不会对训练和测试的准确性产生影响。