Federating Recommendations Using Differentially Private Prototypes
By Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo. 2020
47 citations recorded by the source corpus.
Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users' interaction patterns. However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn such a model without accessing the sensitive data, and without inadvertently leaking private information? We propose a new federated approach to learning global and local private models for recommendation without collecting raw data, user statistics, or information about personal preferences. Our method produces a set of prototypes that allows us to infer global behavioral patterns, while providing differential privacy guarantees for users in any database of the system. By requiring only two rounds of communication, we both reduce the communication costs and avoid the excessive privacy loss associated with iterative procedures. We test our framework on synthetic data as well as real federated medical data and Movielens ratings data. We show local adaptation of the global model allows our method to outperform centralized matrix-factorization-based recommender system models, both in terms of accuracy of matrix reconstruction and in terms of relevance of the recommendations, while maintaining provable privacy guarantees. We also show that our method is more robust and is characterized by smaller variance than individual models learned by independent entities.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Murali Annavaram
- Qiang Yang
- Chaoyang He
- Yang Liu
- S. Avestimehr
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
In Information Systems
Citations in the corpus
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110, 111].”
- Robust Federated Recommendation System (2020, methodology): “Our method is able to handle real-time personalization and sparse client feedback [16] better than the existing methods [1; 10; 29].”
- Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “[30] use K-means clustering to recommend treatments based on datasets about drug administration.”