Date：July 8th, 1:00-5:30 p.m
Venue：Hangzhou Xixi Hotel（Meizhu Hall）
1. Federated learning（1:00~3:00 p.m）
Rui Duan
Assistant Professor of Biostatistics
Harvard T.H. Chan School of Public Health
Title: Principles and Practices of Federated Learning: Methods, Challenges, and Case Studies
Abstract: In many areas, data has been collected in a decentralized way and federated learning emerges as an important methodology for training statistical and machine learning models without the need to centralize data. In this short course, we will delve into the fundamental principles and state-of-the-art techniques of federated learning. We will introduce practical considerations and challenges, including privacy concerns and communication barriers, within real-world scenarios. Additionally, we will discuss innovative strategies to enhance the effectiveness and applicability of federated learning. We will explore case studies that demonstrate the application of federated learning in biomedical research, aimed at facilitating multi-institutional data integration and collaboration.
2. Data visualization and reproducibility（3:30~5:30 p.m）
Subtitle: Have a Shiny Day!
Topic: Mini-course on R Shiny for data visualization and reproducibility
Schedule:
1. Introduction to the mini-course; Andre Python, ZJU100 Young Professor, Zhejiang University; 5 minutes
2. Introduction to R Shiny; Tutor: Kimberly Zhang, Senior Data Scientist, Microsoft; 45 minutes
3. Shiny scalability; Tutor: Yang Ming, Data Scientist, SZMS Technology; 45 minutes
4. Shiny reproducibility; Tutor: Tim CD Lucas, Lecturer, University of Leicester ; 20 minutes
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