mlsanityData to 7 Oct 2026

A Privacy Preserving System for Movie Recommendations Using Federated Learning

arXiv 2303.04689 ↗First posted 7 Mar 2023cs.IR cs.CR cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 45 of its 102 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

45 of 102 references.

References · 45

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33 more
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Cited by year

Citing papers per year: 2024: 6.

6 papers.

  1. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  2. 2024Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosiscited by 4
  3. 2024Optimizing Federated Learning by Entropy-Based Client Selectioncited by 3
  4. 2024FedSlate:A Federated Deep Reinforcement Learning Recommender Systemcited by 1
  5. 2024Assistive AI for Augmenting Human Decision-makingcited by 0
  6. 2024LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Modelcited by 0