ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

Abstract

Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent research on upscaling and privatizing personalized recommendation services, using on-device data to learn recommender models locally. These models are then aggregated globally to obtain a more performant model, while maintaining data privacy. Typically, federated recommender systems (FRSs) do not take into account the lack of resources and data availability at the end-devices. In addition, they assume that the interaction data between users and items is i.i.d. and stationary across end-devices (i.e., users), and that all local recommender models can be directly averaged without considering the user’s behavioral diversity. However, in real scenarios, recommendations have to be made on end-devices with sparse interaction data and limited resources. Furthermore, users’ preferences are heterogeneous and they frequently visit new items. This makes their personal preferences highly skewed, and the straightforwardly aggregated model is thus ill-posed for such non-i.i.d. data. In this paper, we propose Resource Efficient Federated Recommender System (ReFRS) to enable decentralized recommendation with dynamic and diversified user preferences. On the device side, ReFRS consists of a lightweight self-supervised local model built upon the variational autoencoder for learning a user’s temporal preference from a sequence of interacted items. On the server side, ReFRS utilizes a scalable semantic sampler to adaptively perform model aggregation within each identified cluster of similar users. The clustering module operates in an asynchronous and dynamic manner to support efficient global model update and cope with shifting user interests. As a result, ReFRS achieves superior performance in terms of both accuracy and scalability, as demonstrated by comparative experiments on real datasets.

Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

“…✓ Supported × Not supported or unavailable 1 In comparison with retraining the model from its initial state. 2 All remaining clients possess a usable model (ensemble) before unlearning finishes.…”

“…The surge of edge computing and big data brings people personalized services in various domains like product recommendation 1 and personalized healthcare analysis 2 . In those services, users' edge devices (e.g., smartphones and smartwatches) play an important role in collecting user data and generating analytical feedback [3][4][5] , while providing a security and timeliness advantage compared with the outgoing centralized services.…”

Interaction-level Membership Inference Attack Against Federated Recommender Systems

“…For example, FedFast [24] aims to accelerate the convergence of FedRec training. Imran et al [13] and Wang et al [35] focused on the efficiency of FedRecs.…”

Untangling the Role of Data Processing in Enhancing Recommender Systems Performance

“…A lapse in these steps can lead to subpar system performance, decreased user trust, and, consequently, a potential decline in platform engagement [4]. Despite their pivotal role, the intricacies of these processes and their direct impact on the efficacy of recommender systems remain undervalued in many discussions and implementations [5].…”