# Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

arXiv: https://arxiv.org/abs/2109.05446 · HTML: https://mlsanity.com/paper/2109.05446/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2021.

## Built on by (most cited first, 21 of 21)

- [FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning](https://mlsanity.com/paper/2208.12268/) (2022)
- [FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models](https://mlsanity.com/paper/2406.04845/) (2024)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training](https://mlsanity.com/paper/2401.03748/) (2024)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [Doubly Adversarial Federated Bandits](https://mlsanity.com/paper/2301.09223/) (2023)
- [Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework](https://mlsanity.com/paper/2301.00489/) (2023)
- [Clip Body and Tail Separately: High Probability Guarantees for DPSGD with Heavy Tails](https://mlsanity.com/paper/2405.17529/) (2024)
- [Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs](https://mlsanity.com/paper/2402.14609/) (2024)
- [Robust Quantity-Aware Aggregation for Federated Learning](https://mlsanity.com/paper/2205.10848/) (2022)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Federated Recommender System with Data Valuation for E-commerce Platform](https://mlsanity.com/paper/2509.11196/) (2025)
- [Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation](https://mlsanity.com/paper/2409.17711/) (2024)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement](https://mlsanity.com/paper/2607.27659/) (2026)
- [FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization](https://mlsanity.com/paper/2409.00116/) (2024)
- [Privacy-Preserving Multimodal News Recommendation through Federated Learning](https://mlsanity.com/paper/2507.15460/) (2025)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.