Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

Abstract

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user' privacy, in contrast to the traditional paradigm of collecting, storing and processing user data on a backend server beyond the user's control. In this paper we introduce, as far as we are aware, the first federated implementation of a Collaborative Filter. The federated updates to the model are based on a stochastic gradient approach. As a classical case study in machine learning, we explore a personalized recommendation system based on users' implicit feedback and demonstrate the method's applicability to both the MovieLens and an in-house dataset. Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application while maintaining recommender performance.

12 pages, 2 figures, 2 tables, submitted to a conference

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• FedML: A Research Library and Benchmark for Federated Machine Learning
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• Federated Neural Collaborative Filtering
• Federated Social Recommendation with Graph Neural Network
• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
• Variational Federated Multi-Task Learning
• FedRecAttack: Model Poisoning Attack to Federated Recommendation
• Federated Multi-view Matrix Factorization for Personalized Recommendations
• A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
• Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
• Federated Learning Meets Natural Language Processing: A Survey
• On the User Behavior Leakage from Recommender System Exposure
• Federated User Representation Learning
• Federated Continual Learning: Concepts, Challenges, and Solutions
• A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
• How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
• Federated Reconstruction: Partially Local Federated Learning
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack
• Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
• Real-time Federated Evolutionary Neural Architecture Search
• A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
• Fast-adapting and Privacy-preserving Federated Recommender System
• PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems
• Privacy-Preserving News Recommendation Model Learning
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
• Preventing the Popular Item Embedding Based Attack in Federated Recommendations
• Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach
• Practical and Secure Federated Recommendation with Personalized Masks
• Federated Multi-Armed Bandits
• A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
• Real-time Short Video Recommendation on Mobile Devices
• Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
• Meta Matrix Factorization for Federated Rating Predictions
• Inferring Communities of Interest in Collaborative Learning-based Recommender Systems