# A Payload Optimization Method for Federated Recommender Systems

arXiv: https://arxiv.org/abs/2107.13078 · HTML: https://mlsanity.com/paper/2107.13078/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach](https://mlsanity.com/paper/2001.04756/) (2020)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [Thompson Sampling for Complex Bandit Problems](https://mlsanity.com/paper/1311.0466/) (2013)
- [An Information-Theoretic Analysis for Thompson Sampling with Many Actions](https://mlsanity.com/paper/1805.11845/) (2018)
- [Federated Multi-view Matrix Factorization for Personalized Recommendations](https://mlsanity.com/paper/2004.04256/) (2020)
- [A Novel Privacy-Preserved Recommender System Framework based on Federated Learning](https://mlsanity.com/paper/2011.05614/) (2020)

## Built on by (most cited first, 10 of 10)

- [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)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation](https://mlsanity.com/paper/2205.02359/) (2022)
- [FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings](https://mlsanity.com/paper/2210.13686/) (2022)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applications](https://mlsanity.com/paper/2604.09276/) (2026)
- [Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing](https://mlsanity.com/paper/2507.08842/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

We introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and users depends on the number of items to recommend. The model payload grows when there is an increasing number of items. This becomes challenging for an FRS if it is running in production mode. To tackle the payload challenge, we formulated a multi-arm bandit solution that selected part of the global model and transmitted it to all users. The selection process was guided by a novel reward function suitable for FL systems. So far as we are aware, this is the first optimization method that seeks to address item dependent payloads. The method was evaluated using three benchmark recommendation datasets. The empirical validation confirmed that the proposed method outperforms the simpler methods that do not benefit from the bandits for the purpose of item selection. In addition, we have demonstrated the usefulness of our proposed method by rigorously evaluating the effects of a payload reduction on the recommendation performance degradation. Our method achieved up to a 90% reduction in model payload, yielding only a ~4% - 8% loss in the recommendation performance for highly sparse datasets