在边缘设备上实现隐私保护的联邦学习，提升应用推荐模型精度。
Private Federated Learning In Real World Application -- A Case Study
- 通过联邦学习将用户数据留在本地，仅上传模型更新。
- 模型准确率随用户行为变化持续优化，保持隐私合规。
- 适合关注数据隐私的移动应用与智能设备厂商。
本文展示了一种在边缘设备上使用私有联邦学习（PFL）进行机器学习模型训练的实现方案。我们提出一种新框架，利用PFL解决使用用户私有数据训练模型的挑战。该框架确保用户数据始终保留在个人设备上，仅传输必要的模型更新至中心服务器进行聚合，同时提供隐私保障。我们详细描述了应用选择模型的架构，该模型采用带注意力机制的神经网络，并通过不确定性管理处理模糊性。通过离线仿真和设备端训练实验，验证了该方法在真实场景中的可行性。结果表明，PFL能够通过适应用户行为变化，提升应用选择模型的准确性，同时满足隐私标准。本研究为希望实施PFL的行业提供了可靠策略，支持在边缘设备上直接训练预测模型并保障用户数据隐私。
原文摘要 · Abstract (English)
This paper presents an implementation of machine learning model training using private federated learning (PFL) on edge devices. We introduce a novel framework that uses PFL to address the challenge of training a model using users' private data. The framework ensures that user data remain on individual devices, with only essential model updates transmitted to a central server for aggregation with privacy guarantees. We detail the architecture of our app selection model, which incorporates a neural network with attention mechanisms and ambiguity handling through uncertainty management. Experiments conducted through off-line simulations and on device training demonstrate the feasibility of our approach in real-world scenarios. Our results show the potential of PFL to improve the accuracy of an app selection model by adapting to changes in user behavior over time, while adhering to privacy standards. The insights gained from this study are important for industries looking to implement PFL, offering a robust strategy for training a predictive model directly on edge devices while ensuring user data privacy.
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