  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.
