Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analytics. Nevertheless, in the classic cloud-based/centralized e-health paradigms, all the data will be centrally stored on the server to facilitate model training, which inevitably incurs privacy concerns and high time delay. Distributed solutions like Decentralized Stochastic Gradient Descent (D-SGD) are proposed to provide safe and timely diagnostic results based on personal devices. However, methods like D-SGD are subject to the gradient vanishing issue and usually proceed slowly at the early training stage, thereby impeding the effectiveness and efficiency of training. In addition, existing methods are prone to learning models that are biased towards users with dense data, compromising the fairness when providing E-health analytics for minority groups. In this paper, we propose a Decentralized Block Coordinate Descent (D-BCD) learning framework that can better optimize deep neural network-based models distributed on decentralized devices for E-health analytics. As a gradient-free optimization method, Block Coordinate Descent (BCD) mitigates the gradient vanishing issue and converges faster at the early stage compared with the conventional gradient-based optimization. To overcome the potential data scarcity issues for users’ local data, we propose similarity-based model aggregation that allows each on-device model to leverage knowledge from similar neighbor models, so as to achieve both personalization and high accuracy for the learned models. Benchmarking experiments on three real-world datasets illustrate the effectiveness and practicality of our proposed D-BCD, where additional simulation study showcases the strong applicability of D-BCD in real-life E-health scenarios.

Authors

• Guanhua Ye School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing, PR China
• Hongzhi Yin University of Queensland
• Tong Chen
• Miao Xu University of Queensland
• Quoc Viet Hung Nguyen
• Jiangning Song Monash Data Futures Institute, Monash University Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University

Keywords

• Analytical models
• Data models
• Predictive models
• Computational modeling
• Training
• Servers
• Optimization
• Block Coordinate Descent
• Learning Models
• Machine Learning Models
• Local Data
• Stochastic Gradient Descent
• Real-world Datasets
• Vanishing Gradient
• Personal Devices
• Neighbor Model
• Deep Neural Network
• Weight Matrix
• Sleep Apnea
• Multilayer Perceptron
• Sparse Data
• Sensitive Data
• Obstructive Sleep Apnea
• Rapid Eye Movement
• Deep Neural Network Model
• Model Of Personality
• Communication Cost
• eHealth Applications
• Non-rapid Eye Movement
• Central Server
• Excessive Daytime Sleepiness
• Inference Attacks
• Sleep Stages
• Limited Amount Of Data
• eHealth Services
• Communication Graph
• Variable Splitting
• On-device machine learning
• E-health analytics
• decentralized optimization
• similarity-based aggregation
• Computer Simulation
• Humans
• Machine Learning
• Neural Networks, Computer
• Privacy
• Telemedicine