Modern Challenges in Data Decentralization: Federated Learning, Differential Privacy and Communication Constraints

(06 Jul 2026–17 Jul 2026)

Organizing Committee

 

Co-chairs

  • Yi Yu (University of Warwick)
 
 

Contact Information

For scientific queries, please contact: wanjie.wang(AT) nus.edu.sg

Overview

Data explosion, characterized by an increase in both volume and complexity, often leads to data decentralization. This decentralization is partly implemented to facilitate storage and enhance computational feasibility.

Decentralized data lead to the possibility of protecting data owners’ privacy but also brings new challenges in how to efficiently communicate between different data sites and how to aggregate knowledge learned from different sites with potential data heterogeneity. In this workshop, we aim to gather researchers from mathematics, statistics, machine learning, computer science, and application areas, to collectively discuss the caveats of the state-of-the-art methods and theory in decentralized learning, as well as to open new possibilities with great minds from different fields.

Activities

We have the following activity plan:
a. On each Wednesday, there is an outgoing activity in the afternoon; and
b. During the weekend (July 11-12th), we plan an excursion to somewhere outside Singapore at the participants’ own costs.

DateAbstract
Workshop 106–10 JulyN/A
Workshop 213–17 JulyN/A

Registration

Click here to register

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