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HeteFedRec: Federated Recommender Systems with Model Heterogeneity by Yuan, Wei; Qu, Liang; Cui, Lizhen; Tong, Yongxin; Zhou, Xiaofang; Yin, Hongzhi is a scholarly article available to read on EtoBox.

What is HeteFedRec: Federated Recommender Systems with Model Heterogeneity about?

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to participants. In HeteFedRec, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectiv

Author

Yuan, Wei; Qu, Liang; Cui, Lizhen; Tong, Yongxin; Zhou, Xiaofang; Yin, Hongzhi

Published

2023

Language

EN