Federated Learning (FL) is a distributed machine learning framework in communication network systems. However, the systems' Non-Independent and Identically Distributed (Non-IID) data negatively affect the convergence efficiency of the global model, since only a subset of these data samples are beneficial for model convergence. In pursuit of this subset, a reliable approach involves determining a measure of validity to rank the samples within the dataset. In this paper, We propose the BHerd strategy which selects a beneficial herd of local gradients to accelerate the convergence of the FL model. Specifically, we map the distribution of the local dataset to the local gradients and use the Herding strategy to obtain a permutation of the set of gradients, where the more advanced gradients in the permutation are closer to the average of the set of gradients. These top portion of the gradients will be selected and sent to the server for global aggregation. We conduct experiments on different datasets, models and scenarios by building a prototype system, and experimental results demonstrate that our BHerd strategy is effective in selecting beneficial local gradients to mitigate the effects brought by the Non-IID dataset, thus accelerating model convergence.

翻译：联邦学习(FL)是通信网络系统中的一种分布式机器学习框架。然而,系统中非独立同分布(Non-IID)数据会对全局模型的收敛效率产生负面影响,因为仅有部分数据样本对模型收敛有益。为寻找这类样本,一种可靠方法是确定有效性度量标准对数据集样本进行排序。本文提出BHerd策略,通过选择有益的局部梯度群来加速联邦学习模型的收敛。具体而言,我们将局部数据集的分布映射至局部梯度,并采用Herding策略获取梯度集合的排序,其中排序中位置靠前的梯度更接近梯度集合的平均值。这些排名靠前的梯度将被选送至服务器进行全局聚合。我们通过构建原型系统,在不同数据集、模型和场景下开展实验,实验结果表明BHerd策略能有效选择有益的局部梯度,缓解Non-IID数据集带来的影响,从而加速模型收敛。