A Privacy Preserving System for Movie Recommendations Using Federated Learning

• URL: http://arxiv.org/abs/2303.04689v4
• Date: Thu, 16 May 2024 11:03:49 GMT
• Title: A Privacy Preserving System for Movie Recommendations Using Federated Learning
• Authors: David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek,
• Abstract summary: We present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels. It is trained using federated learning and thus, by its very nature, privacy-preserving. 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.
• Score: 12.751432553199628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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