# Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training

arXiv: https://arxiv.org/abs/2401.03748 · HTML: https://mlsanity.com/paper/2401.03748/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization](https://mlsanity.com/paper/1905.13727/) (2019)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.06098/) (2021)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2102.04925/) (2021)
- [Meta Matrix Factorization for Federated Rating Predictions](https://mlsanity.com/paper/1910.10086/) (2019)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [Fast-adapting and Privacy-preserving Federated Recommender System](https://mlsanity.com/paper/2104.00919/) (2021)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization](https://mlsanity.com/paper/2206.11743/) (2022)
- [FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling](https://mlsanity.com/paper/2202.04975/) (2022)
- [Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation](https://mlsanity.com/paper/2109.05446/) (2021)
- [Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism](https://mlsanity.com/paper/2010.08899/) (2020)
- [How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning](https://mlsanity.com/paper/2007.09370/) (2020)
- [FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence](https://mlsanity.com/paper/2112.11134/) (2021)
- [A Payload Optimization Method for Federated Recommender Systems](https://mlsanity.com/paper/2107.13078/) (2021)
- [Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures](https://mlsanity.com/paper/2304.03054/) (2023)

## Built on by (most cited first, 14 of 14)

- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [An Automatic Graph Construction Framework based on Large Language Models for Recommendation](https://mlsanity.com/paper/2412.18241/) (2024)
- [Federated Low-Rank Adaptation for Foundation Models: A Survey](https://mlsanity.com/paper/2505.13502/) (2025)
- [Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2408.14736/) (2024)
- [DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models](https://mlsanity.com/paper/2509.09097/) (2025)
- [Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning](https://mlsanity.com/paper/2508.01348/) (2025)
- [Federated Continual Recommendation](https://mlsanity.com/paper/2508.04792/) (2025)
- [Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates](https://mlsanity.com/paper/2512.12295/) (2025)
- [Communication-Efficient Federated Learning by Exploiting Spatio-Temporal Correlations of Gradients](https://mlsanity.com/paper/2601.10491/) (2026)
- [WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity](https://mlsanity.com/paper/2602.01126/) (2026)
- [An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy](https://mlsanity.com/paper/2503.20768/) (2025)
- [Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing](https://mlsanity.com/paper/2507.08842/) (2025)
- [FedFlex: Federated Learning for Diverse Netflix Recommendations](https://mlsanity.com/paper/2507.21115/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns. However, one of the major challenges in these systems lies in the communication costs that arise from the need to transmit neural network models between user devices and a central server. Prior approaches to these challenges often lead to issues such as computational overheads, model specificity constraints, and compatibility issues with secure aggregation protocols. In response, we propose a novel framework, called Correlated Low-rank Structure (CoLR), which leverages the concept of adjusting lightweight trainable parameters while keeping most parameters frozen. Our approach substantially reduces communication overheads without introducing additional computational burdens. Critically, our framework remains fully compatible with secure aggregation protocols, including the robust use of Homomorphic Encryption. The approach resulted in a reduction of up to 93.75% in payload size, with only an approximate 8% decrease in recommendation performance across datasets. Code for reproducing our experiments can be found at https://github.com/NNHieu/CoLR-FedRec.