Federated learning (FL) is a popular framework for training an AI model using distributed mobile data in a wireless network. It features data parallelism by distributing the learning task to multiple edge devices while attempting to preserve their local-data privacy. One main challenge confronting practical FL is that resource constrained devices struggle with the computation intensive task of updating of a deep-neural network model. To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning. Specifically, in each iteration of the FL algorithm, several subnets are independently generated from the global model at the server using dropout but with heterogeneous dropout rates (i.e., parameter-pruning probabilities), each of which is adapted to the state of an assigned channel. The subsets are downloaded to associated devices for updating. Thereby, FdeDrop reduces both the communication overhead and devices' computation loads compared with the conventional FL while outperforming the latter in the case of overfitting and also the FL scheme with uniform dropout (i.e., identical subsets).

本文提出一种名为 Federated dropout(FedDrop) 的联邦式学习方案，该方案利用经典的dropout算法进行随机模型修剪。该方案可在减少通信开销和设备计算负载的同时，表现出比传统的FL和具有相同概率的子网的FL方案更好的效果。

联邦Dropout——一种简单的方法，在资源受限设备上实现联邦学习