A Privacy Preserving System for Movie Recommendations using Federated Learning

Abstract

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are employed 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. Accordingly, we present a complete recommender system for movie recommendations, which provides privacy and thus trustworthiness on two levels: First, it is trained using federated learning and thus is, by its very nature, privacy-preserving, while still enabling individual users to benefit from global insights. And second, a novel federated learning scheme, FedQ, is employed, which not only addresses the problem of non-i.i.d. and small local datasets, but also prevents input data reconstruction attacks by aggregating client models early. To reduce the communication overhead, compression is applied, which significantly reduces the exchanged neural network updates to a fraction of their original data. We conjecture that it may also improve data privacy through its lossy quantization stage.

Neural Network Coding of Difference Updates for Efficient Distributed Learning Communication

“…In the health sector, FedHealth [60] was proposed as one of the first FL frameworks to accomplish accurate and personalized healthcare by aggregating patient data from wearables in a privacy-preserving manner. Furthermore, FL is used for a range of website applications, such as movie recommendation systems with > 150, 000 clients [61] or personalized ecommerce. For the latter, [62] presents a model that is trained in a distributed way and processes the relationships between users, videos, and products to provide videos online with ads tailored to the user and video semantics.…”

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

“…Recommendation: A recommendation entity in an FRS allows individual users to access personalized recommendations from multiple sources [30,31]. This type of system is useful for organizations that have multiple data sources and need to be able to provide personalized recommendations to individual users based on their historical experience [32].…”