用随机神经网络提升联邦学习抗数据噪声能力
Federated Learning on Stochastic Neural Networks
- 在联邦学习中用随机神经网络做本地模型，可估计真实数据状态
- 能量化本地数据中的隐含噪声，提升模型鲁棒性
- 适合处理非独立同分布数据，尤其对低质量采集数据有效
联邦学习利用客户端设备的边缘计算优化模型，同时通过确保本地数据不离开设备来保护用户隐私。然而，由于所有数据由客户端收集，联邦学习易受本地数据中潜在噪声的影响。测量能力有限或人为误差可能导致客户端数据出现偏差。为解决此问题，我们提出在联邦学习框架中使用随机神经网络作为本地模型。该方法不仅能估计数据的真实潜在状态，还能量化隐含噪声。我们将这一结合随机神经网络的联邦学习方法称为联邦随机神经网络。数值实验表明，该方法在处理非独立同分布数据时具有优异性能和有效性。
原文摘要 · Abstract (English)
Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remains on the device. However, since all data is collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as Federated stochastic neural networks. We will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling non-independent and identically distributed data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。