Edge computing allows artificial intelligence and machine learning models to be deployed on edge devices, where they can learn from local data and collaborate to form a global model. Federated learning (FL) is a distributed machine learning technique that facilitates this process while preserving data privacy. However, FL also faces challenges such as high computational and communication costs regarding resource-constrained devices, and poor generalization performance due to the heterogeneity of data across edge clients and the presence of out-of-distribution data. In this paper, we propose the Gradient-Congruity Guided Federated Sparse Training (FedSGC), a novel method that integrates dynamic sparse training and gradient congruity inspection into federated learning framework to address these issues. Our method leverages the idea that the neurons, in which the associated gradients with conflicting directions with respect to the global model contain irrelevant or less generalized information for other clients, and could be pruned during the sparse training process. Conversely, the neurons where the associated gradients with consistent directions could be grown in a higher priority. In this way, FedSGC can greatly reduce the local computation and communication overheads while, at the same time, enhancing the generalization abilities of FL. We evaluate our method on challenging non-i.i.d settings and show that it achieves competitive accuracy with state-of-the-art FL methods across various scenarios while minimizing computation and communication costs.

边缘计算允许在边缘设备上部署人工智能和机器学习模型，它们可以从本地数据中学习并协作形成全局模型。联邦学习是一种分布式机器学习技术，旨在在保护数据隐私的同时促进此过程。本文提出了一种新方法——梯度一致性引导的联邦稀疏训练（FedSGC），它将动态稀疏训练和梯度一致性检查集成到联邦学习框架中，以解决高计算和通信成本的限制性设备、数据不同和分布不均以及解决数据中存在的局外数据等问题。我们在具有挑战性的非独立同分布环境中评估了我们的方法，并展示了它在各种场景中与最先进的联邦学习方法相比具有竞争力的准确性，同时最大程度地减少计算和通信成本。

梯度同构引导的联邦稀疏训练