Shangqian Gao, Junyi Li, Zeyu Zhang, Yanfu Zhang, Weidong Cai, Heng Huang
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Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources. Typically redundant weights or structures are removed to achieve the target resource budget. Although data-driven pruning approaches have proven to be more effective they cannot be directly applied to federated learning (FL) which has emerged as a popular technique in edge computing applications because of distributed and confidential datasets. In response to this challenge we design a new network pruning method for FL. We propose device-wise sub-networks for each device assuming that the data distribution is similar within each device. These sub-networks are generated through sub-network embeddings and a hypernetwork. To further minimize memory usage and communication costs we permanently prune the full model to remove weights that are not useful for all devices. During the FL process we simultaneously train the device-wise sub-networks and the base sub-network to facilitate the pruning process. We then finetune the pruned model with device-wise sub-networks to regain performance. Moreover we provided the theoretical guarantee of convergence for our method. Our method achieves better performance and resource trade-off than other well-established network pruning baselines as demonstrated through extensive experiments on CIFAR-10 CIFAR-100 and TinyImageNet.