Secure efficient Federated KNN for Recommendation Systems

MPhil Thesis Defence


Title: "Secure efficient Federated KNN for Recommendation Systems"

By

Mr. Zhaorong LIU


Abstract

K-nearest neighbors (KNN) has been successfully used for recommendation, but 
querying neighbors of high quality is nearly impossible when the feature space 
is small and has limited training data. However, due to privacy requirements 
and government policies, directly transferring data from one data owner to 
another is not workable. Therefore, we propose a novel KNN approach, secured 
federated KNN (SF-KNN), that takes privacy requirements into consideration and 
builds a federated model to gain global neighbors with joint parties, in order 
to improve the model performance. Specifically, it empowers the parties to 
train high-quality models with little data. More importantly, it makes 
cross-domain training possible. We implement SF-KNN on Euclidean and cosine 
metrics using user-based and item-based methods. In our experiment, we evaluate 
the proposed SF-KNN on three data sources, MovieLens, Netflix, and Amazon, and 
several diverse domains, movies, books, clothes, jewellery, and food, by 
comparing it against various baselines. The experiment results indicate that 
SF-KNN is able to learn more precise neighbors than a local KNN trained by 
parties individually. In general, it outperforms the local KNN on all of the 
datasets, reaching 15% average accuracy gain on the Euclidean metric and 8% on 
the cosine metric when simulating 10 parties across all data sources.


Date:  			Friday, 8 January 2021

Time:			2:00pm - 4:00pm

Zoom meeting: 
https://hkust.zoom.us/j/91304907404?pwd=azFIS210R3RKZjJTNUt0aDhxVXNxdz09

Committee Members:	Dr. Kai Chen (Supervisor)
 			Dr. Yangqiu Song (Chairperson)
 			Dr. Xiaojuan Ma


**** ALL are Welcome ****