Meta Matrix Factorization for Federated Rating Predictions

Abstract

Federated recommender systems have distinct advantages in terms of privacy protection over traditional recommender systems that are centralized at a data center. With the widespread use and the growing computing power of mobile devices, it is becoming increasingly feasible to store and process data locally on the devices and to train recommender models in a federated manner. However, previous work on federated recommender systems does not fully account for the limitations in terms of storage, RAM, energy and communication bandwidth in a mobile environment. The scales of the models proposed are too large to be easily run on mobile devices. Also, existing federated recommender systems need to fine-tune recommendation models on each device, which makes it hard to effectively exploit collaborative filtering information among users/devices.Our goal in this paper is to design a novel federated learning framework for rating prediction (RP) for mobile environments that operates on par with state-of-the-art fully centralized RP methods. To this end, we introduce a federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF), that is able to generate private item embeddings and RP models with a meta network. Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module. Then, we employ a meta recommender module to generate private item embeddings and a RP model based on the collaborative vector in the server. To address the challenge of generating a large number of high-dimensional item embeddings, we devise a rise-dimensional generation strategy that first generates a low-dimensional item * Co-corresponding author.

Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

“…In this section, we address RQ1a and RQ1b. As such, we repeat experiments by Lin et al [16] to verify the reproducibility of their results. Therefore, we evaluate MetaMF on the four datasets Douban, Hetrec-MovieLens, MovieLens 1M, and Ciao.…”

“…In the following, we present experiments that go beyond reproducing Lin et al's work [16]. Concretely, we explore the robustness of MetaMF against decreasing privacy budgets and discuss RQ2a and RQ2b.…”

“…Similar to recent work [15,5], MetaMF by Lin et al [16] combines federated learning with meta learning to provide personalization and privacy. Besides, MetaMF exploits collaborative information among users and distributes a private rating prediction model to each user.…”

“…We want to highlight that we are not interested in outperforming any stateof-the-art approaches on our five datasets. Thus, we refrain from conducting any hyperparameter tuning or parameter search and utilize precisely the same parameters, hyperparameters, and optimization algorithms as Lin et al [16].…”

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

“…However, many of these attack approaches fundamentally rely on the white-box model, in which the attacker requires the adversary to have full knowledge of the target model and dataset. Another variant of federated recommenders are devised to inhibit the availability of dataset [26,28,30,34]. For federated recommender systems, expecting complete access to the dataset and model is not realistic.…”