  Federated Learning (FL) has emerged as a new paradigm for training machine
learning models distributively without sacrificing data security and privacy.
Learning models on edge devices such as mobile phones is one of the most common
use cases for FL. However, Non-identical independent distributed~(non-IID) data
in edge devices easily leads to training failures. Especially,
over-parameterized machine learning models can easily be over-fitted on such
data, hence, resulting in inefficient federated learning and poor model
performance. To overcome the over-fitting issue, we proposed an adaptive
dynamic pruning approach for FL, which can dynamically slim the model by
dropping out unimportant parameters, hence, preventing over-fittings. Since the
machine learning model's parameters react differently for different training
samples, adaptive dynamic pruning will evaluate the salience of the model's
parameter according to the input training sample, and only retain the salient
parameter's gradients when doing back-propagation. We performed comprehensive
experiments to evaluate our approach. The results show that our approach by
removing the redundant parameters in neural networks can significantly reduce
the over-fitting issue and greatly improves the training efficiency. In
particular, when training the ResNet-32 on CIFAR-10, our approach reduces the
communication cost by 57\%. We further demonstrate the inference acceleration
capability of the proposed algorithm. Our approach reduces up to 50\% FLOPs
inference of DNNs on edge devices while maintaining the model's quality.
