FedCAT：通过设备级联实现精确的联邦学习
FedCAT: Towards Accurate Federated Learning via Device Concatenation
摘要：
联邦学习（Federated Learning，FL）是一种很有前途的分布式机器学习范式，它使所有参与的设备能够协同训练一个全局模型，而不会暴露它们的本地数据隐私。然而，对于非IID场景，FL模型的分类精度急剧下降，由于数据异质性造成的权重发散。虽然已经研究了各种FL变体以提高模型精度，但它们中的大多数仍然遭受不可忽略的通信和计算开销的问题。在本文中，我们介绍了一种新的FL方法命名为Fed-Cat，可以实现高的模型精度的基础上，我们提出的设备选择策略和设备级联为基础的本地训练方法。与聚合在单个设备上训练的本地模型的传统FL方法不同，FedCat在遍历一系列逻辑连接的设备后定期聚合本地模型，这可以有效缓解模型权重发散问题。四个著名的基准测试的综合实验结果表明，我们的方法可以显着提高国家的最先进的FL方法的模型精度，而不会造成额外的通信开销。
英文摘要：
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. However, for non-IID scenarios, the classification accuracy of FL models decreases drastically due to the weight divergence caused by data heterogeneity. Although various FL variants have been studied to improve model accuracy, most of them still suffer from the problem of non-negligible communication and computation overhead. In this paper, we introduce a novel FL approach named Fed-Cat that can achieve high model accuracy based on our proposed device selection strategy and device concatenation-based local training method. Unlike conventional FL methods that aggregate local models trained on individual devices, FedCat periodically aggregates local models after their traversals through a series of logically concatenated devices, which can effectively alleviate the model weight divergence problem. Comprehensive experimental results on four well-known benchmarks show that our approach can significantly improve the model accuracy of state-of-the-art FL methods without causing extra communication overhead.
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