RiM: Record, Improve and Maintain Physical Well-being using Federated Learning
IISER Bhopal（印度比哈尔理工学院）
Aditya Mishra, Haroon Lone
备注：
Report submitted in partial fulfilment of the requirements for the award of the degree of Bachelor of Science (BS) in Electrical Engineering and Computer Science
在学术环境中，高要求的环境往往迫使学生将学业表现置于身体健康之上。此外，隐私担忧和数据泄露的固有风险阻碍了传统机器学习技术在应对这些健康挑战时的部署。在本研究中，我们提出 RiM：Record, Improve, and Maintain，这是一款移动应用，融合了一种新颖的个性化机器学习框架，利用联邦学习通过分析学生的生活习惯来增强其身体健康。我们的方法包括先在大规模模拟数据集上预训练一个多层感知机（MLP）模型，以生成个性化建议。随后，我们采用联邦学习，利用 IISER Bhopal 学生的数据对模型进行微调，从而确保其适用于真实世界场景。该联邦学习方法通过仅共享模型权重而非原始数据来保证差分隐私。实验结果表明，基于 FedAvg 的 RiM 模型取得了 60.71% 的平均准确率和 0.91 的平均绝对误差，优于 FedPer 变体（平均准确率 46.34%，MAE 1.19），从而证明了其在隐私保护约束下预测生活方式缺陷的有效性。
英文摘要：
In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health challenges. In this study, we introduce RiM: Record, Improve, and Maintain, a mobile application which incorporates a novel personalized machine learning framework that leverages federated learning to enhance students' physical well-being by analyzing their lifestyle habits. Our approach involves pre-training a multilayer perceptron (MLP) model on a large-scale simulated dataset to generate personalized recommendations. Subsequently, we employ federated learning to fine-tune the model using data from IISER Bhopal students, thereby ensuring its applicability in real-world scenarios. The federated learning approach guarantees differential privacy by exclusively sharing model weights rather than raw data. Experimental results show that the FedAvg-based RiM model achieves an average accuracy of 60.71% and a mean absolute error of 0.91--outperforming the FedPer variant (average accuracy 46.34%, MAE 1.19)--thereby demonstrating its efficacy in predicting lifestyle deficits under privacy-preserving constraints.
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Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN)（里约格兰德北教育科学和技术联邦学院）
Weill Cornell Medicine（威尔康奈尔医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；U.S. Department of Defense Joint Artificial Intelligence Center（美国国防部联合人工智能中心）；University of Florida（佛罗里达大学）
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of diff…
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University of St.Gallen (HSG)（圣加仑大学（HSG））；Deutsche Bundesbank（德意志联邦银行）
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University of Amsterdam（阿姆斯特丹大学）；Australian National University（澳大利亚国立大学）；University of Sydney（悉尼大学）；N1 Analytics（N1分析公司）；Data61（Data61数据科学研究中心）
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Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National Dental Research Institute Singapore（新加坡国家牙科研究所）；National Dental Centre Singapore（新加坡国家牙科中心）；Oral Health Academic Clinical Programme, Duke-NUS Medical School（杜克-新加坡国立大学医学院口腔健康学术临床项目）；Programme in Health Services and Systems Research, Duke-NUS Medical School（杜克-新加坡国立大学医学院卫生服务与系统研究项目）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Imperial College London（伦敦帝国理工学院）；Singapore General Hospital（新加坡中央医院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
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School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉安吞国际技术学院，泰国 Thammasat 大学）
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Institute of Informatics University of Amsterdam（信息学院阿姆斯特丹大学）；College of Medicine The Ohio State University（医学学院俄亥俄州立大学）；Department of Neuroscience University of California（神经科学系加州大学）；School of Medicine Indiana University（医学院印第安纳大学）
Intel Corporation（英特尔公司）；University of Pennsylvania（宾夕法尼亚大学）
Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and developme…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
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CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；DKFZ German Cancer Research Center（德国癌症研究中心）
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Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, known as multimodal federated learning. However, limitation lies in the predominant approaches as they often assume that each local dataset records samples from all modalities. In this paper, we aim to bridge this gap by proposing an Unimodal…
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The University of Tokyo（东京大学）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National University of Singapore（新加坡国立大学）
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Information Sciences Institute（信息科学研究所）；University of Southern California（南加州大学）
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. For example, data present in users' cellphones, manufacturing data of companies in a given industrial sector, or medical records located at different hospitals. Moreover, participating sites often have different data distributions and computational capabilities. Federated Learning provides an approach to learn a joint model over all the available data in these environments. In this paper, we introduce …
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University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）；New York University（纽约大学）；University of Minnesota（明尼苏达大学）；Halmstad University（哈姆斯塔德大学）；Harvard University（哈佛大学）；University of Florida（佛罗里达大学）
