Federated Learning Infrastructure for Collaborative Machine Learning on Heterogeneous Environments

用于异构环境下协作机器学习的联邦学习基础设施

• 批准号: 22KJ2289
• 负责人: Thonglek Kundjanasith
• 金额: $ 1.09万
• 依托单位: Nara Institute of Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2023
• 资助国家: 日本
• 起止时间: 2023-03-08 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22KJ2289/
• 关键词: Collaborative Develop; Distributed Computing; Edge Machine Learning; Federated Learning; Privacy Preservation; Resource Heterogeneity

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%.

我提出了一个基础设施，允许个人在他们的环境(通常是异质的)上协作开发机器学习模型。拟议的基础设施允许研究人员合作，并有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。我在提议的基础架构中提出了三个组件，以支持高效地培训有关不同存储、计算和网络资源的模型。首先，我提出了一个组件来缩小模型的大小，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类计算资源上训练的模型。第三，我提出了一个组件来稀疏模型，以便在服务器和客户端之间交换模型。建议的基础设施使用最先进的神经网络模型进行评估，以从胸部X光图像中检测新冠肺炎病例。新冠肺炎检测是隐私敏感数据最受欢迎的机器学习应用之一。结果表明，在6个不同的硬件环境上构建的集成模型的准确率比经过训练的单个COVID-Net高5.39%。

项目成果

Automated Quantization and Retraining for Neural Network Models Without Labeled Data

无标记数据的神经网络模型的自动量化和再训练

• DOI: 10.1109/access.2022.3190627
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Kundjanasith Thonglek;Keichi Takahashi;Kohei Ichikawa;Chawanat Nakasan;Hidemoto Nakada;Ryousei Takano;Pattara Leelaprute;Hajimu Iida
• 通讯作者: Hajimu Iida

Kasetsart University(タイ)

农业大学（泰国）

Sparse Communication for Federated Learning

• DOI: 10.1109/icfec54809.2022.00008
• 发表时间: 2022-05
• 期刊: 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)
• 作者: Kundjanasith Thonglek;Keichi Takahashi;Koheix Ichikawa;Chawanat Nakasan;P. Leelaprute;Hajimu Iida