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A Privacy Preserving System for Movie Recommendations Using Federated Learning

Authors:

Karsten Müller,

Wojciech SamekAuthors Info & Claims

ACM Transactions on Recommender Systems

Accepted on 05 November 2023

https://doi.org/10.1145/3634686

Online AM: 24 November 2023 Publication History

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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Becking DMüller KHaase PKirchhoffer HTech GSamek WSchwarz HMarpe DWiegand T(2024)Neural Network Coding of Difference Updates for Efficient Distributed Learning CommunicationIEEE Transactions on Multimedia10.1109/TMM.2024.335719826(6848-6863)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3357198
Chen SShu TZhao HWang JRen SYang L(2024)Free lunch for federated remote sensing target fine-grained classificationKnowledge-Based Systems10.1016/j.knosys.2024.111694294:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111694
Asad MShaukat SJavanmardi ENakazato JTsukada M(2023)A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation SystemsApplied Sciences10.3390/app1310620113:10(6201)Online publication date: 18-May-2023
https://doi.org/10.3390/app13106201

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A Privacy Preserving System for Movie Recommendations Using Federated Learning

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ACM Transactions on Recommender Systems Just Accepted

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Publication History

Online AM: 24 November 2023

Accepted: 05 November 2023

Revised: 20 August 2023

Received: 06 March 2023

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Becking DMüller KHaase PKirchhoffer HTech GSamek WSchwarz HMarpe DWiegand T(2024)Neural Network Coding of Difference Updates for Efficient Distributed Learning CommunicationIEEE Transactions on Multimedia10.1109/TMM.2024.335719826(6848-6863)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3357198
Chen SShu TZhao HWang JRen SYang L(2024)Free lunch for federated remote sensing target fine-grained classificationKnowledge-Based Systems10.1016/j.knosys.2024.111694294:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111694
Asad MShaukat SJavanmardi ENakazato JTsukada M(2023)A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation SystemsApplied Sciences10.3390/app1310620113:10(6201)Online publication date: 18-May-2023
https://doi.org/10.3390/app13106201

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