# Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback

arXiv: https://arxiv.org/abs/2105.03941 · HTML: https://mlsanity.com/paper/2105.03941/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 15 of 15)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2007.15789/) (2020)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection](https://mlsanity.com/paper/2003.10637/) (2020)
- [Federated Heavy Hitters Discovery with Differential Privacy](https://mlsanity.com/paper/1902.08534/) (2019)
- [Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy](https://mlsanity.com/paper/1606.05053/) (2016)
- [Don't let Google know I'm lonely!](https://mlsanity.com/paper/1504.08043/) (2015)

## Built on by (most cited first, 26 of 26)

- [FedRecAttack: Model Poisoning Attack to Federated Recommendation](https://mlsanity.com/paper/2204.01499/) (2022)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling](https://mlsanity.com/paper/2204.14017/) (2022)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Auxo: Efficient Federated Learning via Scalable Client Clustering](https://mlsanity.com/paper/2210.16656/) (2022)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://mlsanity.com/paper/2312.16203/) (2023)
- [FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2207.10265/) (2022)
- [FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation](https://mlsanity.com/paper/2205.02359/) (2022)
- [Cali3F: Calibrated Fast Fair Federated Recommendation System](https://mlsanity.com/paper/2205.13121/) (2022)
- [FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings](https://mlsanity.com/paper/2210.13686/) (2022)
- [FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation](https://mlsanity.com/paper/2305.06272/) (2023)
- [Federated Learning on Non-iid Data via Local and Global Distillation](https://mlsanity.com/paper/2306.14443/) (2023)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [Preventing the Popular Item Embedding Based Attack in Federated Recommendations](https://mlsanity.com/paper/2502.12958/) (2025)
- [Modeling Global Distribution for Federated Learning with Label Distribution Skew](https://mlsanity.com/paper/2212.08883/) (2022)
- [Smart Multi-tenant Federated Learning](https://mlsanity.com/paper/2207.04202/) (2022)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX](https://mlsanity.com/paper/2207.05079/) (2022)
- [DRIFT: A Federated Recommender System with Implicit Feedback on the Items](https://mlsanity.com/paper/2304.09084/) (2023)
- [FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems](https://mlsanity.com/paper/2310.20193/) (2023)
- [Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces](https://mlsanity.com/paper/2311.01722/) (2023)
- [Navigating the Future of Federated Recommendation Systems with Foundation Models](https://mlsanity.com/paper/2406.00004/) (2024)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)

## Abstract (arXiv)

Recommender systems are commonly trained on centrally collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender's collection and handling of potentially sensitive data. Several privacy-aware recommender systems have been proposed in recent literature, but comparatively little attention has been given to systems at the intersection of implicit feedback and privacy. To address this shortcoming, we propose a practical federated recommender system for implicit data under user-level local differential privacy (LDP). The privacy-utility trade-off is controlled by parameters ε and k, regulating the per-update privacy budget and the number of ε-LDP gradient updates sent by each user respectively. To further protect the user's privacy, we introduce a proxy network to reduce the fingerprinting surface by anonymizing and shuffling the reports before forwarding them to the recommender. We empirically demonstrate the effectiveness of our framework on the MovieLens dataset, achieving up to Hit Ratio with K=10 (HR@10) 0.68 on 50k users with 5k items. Even on the full dataset, we show that it is possible to achieve reasonable utility with HR@10>0.5 without compromising user privacy.