A Privacy Preserving System for Movie Recommendations Using Federated Learning

TL;DR

This paper introduces a privacy-preserving movie recommender system using federated learning, employing a novel scheme called FedQ that addresses data heterogeneity, prevents reconstruction attacks, and reduces communication costs.

Contribution

It proposes FedQ, a new federated learning approach for recommender systems that enhances privacy, handles non-i.i.d. data, and reduces communication overhead.

Findings

FedQ effectively mitigates data heterogeneity issues.

The system prevents input data reconstruction attacks.

Compression significantly reduces communication costs.

Abstract

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.