标题： 从噪声分散数据中协同学习联邦模型 显示英文标题

标题： Collaboratively Learning Federated Models from Noisy Decentralized Data

摘要： 联邦学习（FL）已成为一种突出的方法，用于使用边缘设备上的本地数据协作训练机器学习模型，同时保持数据的去中心化。 然而，在FL中考虑本地客户端贡献的数据质量仍然是一个关键挑战，因为本地数据往往容易受到各种噪声和扰动的影响，这会损害聚合过程并导致性能不佳的全局模型。 在本工作中，我们专注于解决输入空间中的噪声数据问题，这与标签噪声相比是一个研究不足的领域。 我们提出了一种基于在噪声和干净输入数据上训练的模型的梯度范数分布密度之间观察到的显著差异的客户端输入综合评估方法。 基于这一观察，我们引入了一种简单而有效的方法，在FL的初始阶段识别数据质量低的客户端。 此外，我们提出了一种感知噪声的FL聚合方法，即联邦噪声筛选（FedNS），该方法可以作为插件方法与广泛使用的FL策略结合使用。 我们在不同联邦设置下的多种基准数据集上进行了广泛的评估，证明了FedNS的有效性。 我们的方法可以轻松地与现有的FL策略集成，在从噪声去中心化数据学习时，使全局模型的性能在 IID 设置下提高高达 13.68%，在 non-IID 设置下提高高达 15.85%。 显示英文摘要

摘要： Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentralized. However, accounting for the quality of data contributed by local clients remains a critical challenge in FL, as local data are often susceptible to corruption by various forms of noise and perturbations, which compromise the aggregation process and lead to a subpar global model. In this work, we focus on addressing the problem of noisy data in the input space, an under-explored area compared to the label noise. We propose a comprehensive assessment of client input in the gradient space, inspired by the distinct disparity observed between the density of gradient norm distributions of models trained on noisy and clean input data. Based on this observation, we introduce a straightforward yet effective approach to identify clients with low-quality data at the initial stage of FL. Furthermore, we propose a noise-aware FL aggregation method, namely Federated Noise-Sifting (FedNS), which can be used as a plug-in approach in conjunction with widely used FL strategies. Our extensive evaluation on diverse benchmark datasets under different federated settings demonstrates the efficacy of FedNS. Our method effortlessly integrates with existing FL strategies, enhancing the global model's performance by up to 13.68% in IID and 15.85% in non-IID settings when learning from noisy decentralized data.