# A Systematic Survey on Federated Sequential Recommendation

arXiv: https://arxiv.org/abs/2504.05313 · HTML: https://mlsanity.com/paper/2504.05313/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Federated Recommendation via Hybrid Retrieval Augmented Generation](https://mlsanity.com/paper/2403.04256/) (2024)
- [ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences](https://mlsanity.com/paper/2207.13897/) (2022)
- [FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence](https://mlsanity.com/paper/2112.11134/) (2021)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services](https://mlsanity.com/paper/2410.04927/) (2024)
- [Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation](https://mlsanity.com/paper/2208.10692/) (2022)
- [FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning](https://mlsanity.com/paper/2309.08420/) (2023)
- [PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System](https://mlsanity.com/paper/2406.05387/) (2024)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [Towards Personalized Federated Multi-Scenario Multi-Task Recommendation](https://mlsanity.com/paper/2406.18938/) (2024)

## Built on by (most cited first, 4 of 4)

- [Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning](https://mlsanity.com/paper/2507.10348/) (2025)
- [Geometric Prior-Guided Federated Prompt Calibration](https://mlsanity.com/paper/2512.07208/) (2025)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Unbiased Rectification for Sequential Recommender Systems Under Fake Orders](https://mlsanity.com/paper/2604.08550/) (2026)

## Abstract (arXiv)

Sequential recommendation is an advanced recommendation technique that utilizes the sequence of user behaviors to generate personalized suggestions by modeling the temporal dependencies and patterns in user preferences. However, it requires a server to centrally collect users' data, which poses a threat to the data privacy of different users. In recent years, federated learning has emerged as a distributed architecture that allows participants to train a global model while keeping their private data locally. This survey pioneers Federated Sequential Recommendation (FedSR), where each user joins as a participant in federated training to achieve a recommendation service that balances data privacy and model performance. We begin with an introduction to the background and unique challenges of FedSR. Then, we review existing solutions from two levels, each of which includes two specific techniques. Additionally, we discuss the critical challenges and future research directions in FedSR.