Federated Learning for Facilitating Privacy Preserving Collaboration

Due to privacy and data confidentiality concerns, today’s insurance industry is rife with the protectionism of proprietary data, which has become a major roadblock that prevents the free flow of data and collaborations between data scientists and analysts. The inaccessibility of data across the boundaries of insurance firms or even business divisions within a corporation makes it difficult to develop comparative analysis and to uncover business insights that can only be learned from the aggregation of data across the board.

Federated learning has been proposed in recent years as a privacy-preserving solution to collaborative machine learning tasks, and it allows data owners to collectively build a model without sharing sensitive data with each other. This technique has seen success in a variety of scenarios, such as healthcare, content recommendation, and smart transportation, and therefore, it has the potential to make an impact on addressing the data concerns in the insurance industry. In this project, we will learn and implement some popular federated learning algorithms and explore their potential insurance applications. 

Supervisors:  Runhuan Feng, Zhiyu (Frank) Quan
Graduate Supervisor: Linfeng Zhang, Panyi Dong