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Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan
TL;DR
Personalized recommendation needs useful user data, but privacy requirements constrain centralized collection and storage. The paper introduces a stochastic-gradient Federated Collaborative Filter that keeps interaction data on clients, and reports statistically similar recommendation performance to standard collaborative filtering. The authors conclude that federated recommendation can preserve user privacy while maintaining recommendation quality.
Problem
Privacy requirements and the risks of inaccurate personalization motivate recommendation methods that avoid collecting and storing users’ personal data centrally.
Method
The paper federates collaborative filtering by distributing model computation to clients and aggregating stochastic-gradient updates to update a server-side master model.
Results
The federated collaborative filter achieves statistically similar recommendation performance to the standard collaborative-filtering model.
Takeaways & Limitations
The results establish that federated personalized recommendations can provide similar quality while preserving users’ privacy.
Takeaways & Limitations
Future evaluation should examine continuously asynchronous client updates, communication efficiency, and security against attacks and threats.
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
The paper addresses the tension between privacy requirements and data-hungry personalized recommendation by introducing federated collaborative filtering. It reports that the approach preserves recommendation accuracy while keeping user data on client devices.
- Privacy regulations increasingly require explicit user consent for collecting and using personal data, potentially reducing data available for machine learning.GDPR changes data collection from a default opt-in to explicit opt-in, and low opt-in rates can reduce model performance.
- Personalized recommendation can produce harmful outcomes when predictions are inaccurate, motivating privacy-by-design methods that avoid centralized data collection.The paper contrasts potential consequences in healthcare and e-commerce settings.
- Federated learning trains models across user devices so private data remains on clients, but prior methods focused on deep learning applications.The paper positions collaborative filtering as a distinct model class requiring a federated implementation.
- The paper introduces the first Federated Collaborative Filter using stochastic gradient updates aggregated from clients to update a master model.The method is derived for implicit-feedback collaborative filtering and is evaluated on simulated, MovieLens, and in-house datasets.
- The authors report that collaborative filtering can be federated without loss of accuracy while enhancing user privacy.This is presented as the paper’s central empirical contribution for personalized recommendation.
2 RELATED WORK
The paper connects matrix factorization and federated learning by adapting stochastic-gradient collaborative filtering to privacy-preserving distributed recommendation. Its central distinction is federating collaborative filtering rather than only deep learning models.
- Matrix factorization, distributed computing, and federated learning form the three research areas at the intersection of this work.The paper situates collaborative filtering within these related technical traditions.
- Matrix factorization: Alternating Least Squares updates one latent factor matrix while holding the other fixed, repeating epochs until a convergence criterion is satisfied.ALS is described as well suited for parallelization despite cubic per-epoch complexity in the number of factors.
- Matrix factorization: Stochastic Gradient Descent solves latent factor matrices through repeated small gradient steps while fixing the other factor matrix.The paper describes SGD as efficient and simple for large-scale recommender applications.
- Federated learning: Federated learning commonly distributes model weights to clients, which update them locally and return updates for server-side aggregation.This standard weighted-averaging pattern has been widely used for deep neural networks.
- Novelty: In the federated collaborative-filtering design, the server distributes item factors while clients retain user factors and send item-factor gradients back for aggregation.The figure describes the division of model state and computation between server and clients.
- Novelty: The proposed work blends SGD-based distributed matrix factorization with privacy-preserving federated learning for collaborative filtering.This extends federated learning beyond the deep-learning models emphasized in earlier applications.
3 Collaborative Filter
The collaborative filter represents sparse user–item interactions with low-dimensional latent factor matrices and predicts preferences from their combination. Implicit feedback uses binary preferences and confidence weighting to handle unobserved interactions.
- Model formulation: Collaborative filtering models user–item interactions with low-rank factorization, addressing highly sparse interaction matrices.The paper considers settings with millions of users and thousands of items, where each user typically interacts with few items.
- Model formulation: The model approximates the interaction matrix as R ∼ X^T Y, where X and Y are low-dimensional latent factor matrices.The factorization uses K latent dimensions.
- Model formulation: For an unspecified user–item interaction, the prediction is computed from the user and item latent factors.The paper presents this prediction in the implicit-feedback setting.
- Implicit feedback: Implicit feedback introduces binary preference variables to represent whether a user prefers an item.Observed interactions include actions such as watching a video or purchasing an item.
- Implicit feedback: Because an unobserved interaction may indicate disinterest or lack of awareness, the model uses a confidence parameter to represent this uncertainty.The confidence parameter is incorporated into the optimization objective.
- Optimization: Alternating Least Squares alternates updates to X and Y across multiple epochs until a suitable convergence criterion is met.The method provides an optimization procedure for the latent factor matrices.
4 Federated Collaborative Filtering
The Federated Collaborative Filter keeps user interaction data on clients while distributing item-factor updates between clients and the server. Client-side gradient contributions are aggregated on the server, enabling federated collaborative filtering without requiring user identities at the server.
- FCF preserves privacy by keeping user interaction data on clients while distributing model computation between clients and the server.The method was introduced to federate collaborative filtering without transferring private user interaction data.
- Item factor vectors are updated on the server and then distributed to clients, whereas user factor vectors are updated locally using each user’s data.This separates server-side item updates from client-side user updates.
- Clients independently calculate item-gradient contributions, which the server aggregates before updating item factors.The client contributions replace the user-wide summation needed for centralized item-factor updates.
- Each FCF epoch updates X once and performs several gradient-descent steps to update Y.Multiple gradient-descent iterations are used because Y is optimized through gradient updates.
- The privacy-preserving solution does not require the server to know user identities because it aggregates client updates without identity references.The server receives aggregated gradient information rather than identity-linked updates.
5 DATA
The study evaluates FCF on simulated interactions, MovieLens ratings converted to implicit feedback, and an anonymized in-house view-event dataset. The datasets impose sparsity or activity thresholds and are summarized in Table 1.
- Simulated data: The simulated dataset is an 80% sparse binary user-item interaction matrix with at least eight views per user and one view per item.Its dimensionality is reported in Table 1.
- MovieLens: The MovieLens dataset contains 1,000,209 ratings from 6,040 users across 3,952 movies and is converted from explicit ratings to implicit feedback.The conversion assumes users watched movies they rated and were unaware of the remaining movies.
- Table 1 reports dataset overviews, with # view events denoting the minimum number of videos watched by each user.
- In-house data: The in-house dataset uses anonymized view events from users who watched more than 20 videos and videos viewed by more than 100 users.The resulting dataset overview is listed in Table 1.
6 EXPERIMENTS AND RESULTS
Experiments evaluate FCF convergence to the standard CF solution and compare recommendation performance across simulated and real datasets. FCF converges robustly with suitable learning-rate control and achieves recommendation metrics comparable to CF.
- Convergence Analysis: FCF alternates an ALS-style X update with several stochastic-gradient updates to Y within each epoch.The Y update requires multiple gradient-descent iterations to reach its optimum.
- Convergence Analysis: ≈0% difference from CF was reached after 5 gradient-descent iterations in the one-epoch experiment.Both models started from the same factors, and X was updated identically before comparing Y.
- Convergence Analysis: γ = 0.05 was stable for α ∈{1, 2, 3, 4}, while halving γ stabilized convergence across the tested α values.The comparison varied the learning rate γ while increasing the implicit-confidence parameter α.
- Convergence Analysis: 5 of 6 FCF runs converged to the CF solution within 6 epochs at α = 10, although convergence rates varied.All six runs reached the optimal CF solution, indicating robust but initialization-dependent convergence.
- Convergence Analysis: Adam stabilized convergence across α = 1−1000 and reached the CF solution within 6−7 epochs at α = 10 and γ = 0.2.The selected Adam settings were β1 = 0.4, β2 = 0.99, and γ = 0.2; convergence occurred around ∼10 iterations in the single-epoch test.
- Recommendation Performance: Less than 0.5% average difference separated CF and FCF across Precision, Recall, F1, MAP, and RMSE test metrics.Table 2 averaged results across users over 10 model rebuilds for two real datasets and one simulated dataset; standard deviations were also small.
7 CONCLUSION
The federated collaborative filter provides recommendation quality comparable to the standard method while preserving user privacy. The authors position the work as an initial step requiring further evaluation of practical deployment and security.
- The federated collaborative filter achieves statistically similar recommendation performance to the standard method while preserving user privacy.
- The method federates standard collaborative filtering through a stochastic gradient descent-based approach.
- The authors identify asynchronous online learning, communication efficiency, and federated-model security as priorities for future work.