Federated Learning Infrastructure for Collaborative Machine Learning on Heterogeneous Environments

我提出了一个基础设施，允许个人在他们的环境中协作开发机器学习模型，这些环境通常是异构的。拟议的基础设施允许研究人员共同努力，并可能建立比大公司更好的模型。拟议的基础设施应用联邦学习来训练模型，同时保护数据隐私。我在建议的基础设施中提出了三个组件，以支持在不同的存储、计算和网络资源上有效地训练模型。首先，我提出了一个组件，以减少模型的大小，以适应异构环境的存储容量。其次，我提出了一个组件来聚合在异构计算资源上训练的模型。第三，我提出了一个组件来稀疏化模型，以便在服务器和客户端之间交换模型。使用最先进的神经网络模型评估了拟议的基础设施，以从胸部X光图像中检测COVID-19病例。COVID-19检测是针对隐私敏感数据的最受欢迎的机器学习应用之一。因此，在所提出的基础设施的六个不同硬件环境上具有异构结构的集成模型产生的准确性比训练的单个COVID-NET高5.39%。

I proposed an infrastructure to allow individuals to collaboratively develop machine learning models on their environments, which are usually heterogeneous.The proposed infrastructure allows researchers to work together and potentially build better models than big companies can. The proposed infrastructure applied federated learning to train the models while preserving data privacy. I proposed three components in the proposed infrastructure to support training a model on diverse storage, computing, and network resources efficiently. First, I proposed a component to reduce the model size to fit the storage capacity of the heterogeneous environment. Second, I proposed a component to aggregate the models trained on heterogeneous computing resources. Third, I proposed a component to sparsify the model for exchanging the models between a server and clients. The proposed infrastructure was evaluated using state-of-the-art neural network models to detect COVID-19 cases from chest X-ray images. COVID-19 detection is one of the most popular machine learning applications for privacy-sensitive data. As a result, the ensemble model with heterogeneous structures on six different hardware environments from the proposed infrastructure produces accuracy higher than a trained single COVID-NET by 5.39%.