Poster: Ensemble Federated Edge Learning for Recommender Systems

Abstract

Given the explosion of e-services, it has become critical for recommender systems (RSs) to have expected suggestions. Traditional machine learning-based recommending models provide an interface for platforms to find the most relevant items for users. Nonetheless, those models are often trained with user data from a single domain at centralized cloud, which hinders the performance of RSs, causes significant data transmission overhead, and may harm data privacy. To address these issues, in this poster, we propose an ensemble federated edge learning scheme (eFEEL) on the basis of a semi-distributed architecture design. eFEEL aims to efficiently and effectively improve RSs without breaching user data privacy.