论文标题
在微控制器上分裂联邦学习：一个关键字斑点展示柜
Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase
论文作者
论文摘要
如今，AI公司通过积极收集Edge设备生成的用户数据来提高服务质量，从而危及数据隐私。为了防止这种情况，将联合学习作为一种私人学习方案提出，使用用户可以在本地训练模型而无需将用户的原始数据收集到服务器。但是，对于具有硬存储器约束的边缘设备上的机器学习应用程序，使用FL实现大型模型是不可行的。为了满足内存需求，最新的协作学习方案名为Split Federal学习是一个潜在的解决方案，因为它可以在设备上保留一个小型模型并将其余模型保留在服务器上。在这项工作中，我们在Arduino董事会上实现了一个简单的SFL框架，并在中国数字数字数据集上验证其正确性，以使用超过90％的准确性。此外，与最先进的FL实施相比，在英语数字音频数据集中，我们的SFL实施的精度提高了13.89％。
Nowadays, AI companies improve service quality by aggressively collecting users' data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers. However, for machine-learning applications on edge devices that have hard memory constraints, implementing a large model using FL is infeasible. To meet the memory requirement, a recent collaborative learning scheme named split federal learning is a potential solution since it keeps a small model on the device and keeps the rest of the model on the server. In this work, we implement a simply SFL framework on the Arduino board and verify its correctness on the Chinese digits audio dataset for keyword spotting application with over 90% accuracy. Furthermore, on the English digits audio dataset, our SFL implementation achieves 13.89% higher accuracy compared to a state-of-the-art FL implementation.
