# Hide Your Model: A Parameter Transmission-free Federated Recommender System

arXiv: https://arxiv.org/abs/2311.14968 · HTML: https://mlsanity.com/paper/2311.14968/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2023.

## Built on by (most cited first, 9 of 9)

- [Tackling Data Heterogeneity in Federated Time Series Forecasting](https://mlsanity.com/paper/2411.15716/) (2024)
- [Efficient Federated Learning with Timely Update Dissemination](https://mlsanity.com/paper/2507.06031/) (2025)
- [Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification](https://mlsanity.com/paper/2404.15585/) (2024)
- [Watermarking Recommender Systems](https://mlsanity.com/paper/2407.21034/) (2024)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [LLM-driven Multimodal Recommendation](https://mlsanity.com/paper/2602.05474/) (2026)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [Improving Federated Graph Recommendation with Semantic Guidance](https://mlsanity.com/paper/2606.15277/) (2026)

## Abstract (arXiv)

With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities. Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters. Nevertheless, this learning framework has two drawbacks that limit its practical usability: (1) It necessitates a global-sharing recommendation model; however, in real-world scenarios, information related to the recommender model, including its algorithm and parameters, constitutes the platforms' intellectual property. Hence, service providers are unlikely to release such information actively. (2) The communication costs of model parameter transmission are expensive since the model parameters are usually high-dimensional matrices. With the model size increasing, the communication burden will be the bottleneck for such traditional FedRecs. Given the above limitations, this paper introduces a novel parameter transmission-free federated recommendation framework that balances the protection between users' data privacy and platforms' model privacy, namely PTF-FedRec. Specifically, participants in PTF-FedRec collaboratively exchange knowledge by sharing their predictions within a privacy-preserving mechanism. Through this way, the central server can learn a recommender model without disclosing its model parameters or accessing clients' raw data, preserving both the server's model privacy and users' data privacy. Besides, since clients and the central server only need to communicate prediction scores which are just a few real numbers, the overhead is significantly reduced compared to traditional FedRecs. The code is available aturlhttps://github.com/hi-weiyuan/PTF-FedRec.