# Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks

arXiv: https://arxiv.org/abs/2207.00755 · HTML: https://mlsanity.com/paper/2207.00755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 2 Jul 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup](https://mlsanity.com/paper/2006.09801/) (2020)
- [Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities](https://mlsanity.com/paper/1708.06698/) (2017)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [Big Data Caching for Networking: Moving from Cloud to Edge](https://mlsanity.com/paper/1606.01581/) (2016)
- [Predicting popularity of online videos using Support Vector Regression](https://mlsanity.com/paper/1510.06223/) (2015)
- [Revocable Federated Learning: A Benchmark of Federated Forest](https://mlsanity.com/paper/1911.03242/) (2019)
- [Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud](https://mlsanity.com/paper/2009.10918/) (2020)
- [Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks](https://mlsanity.com/paper/2011.08449/) (2020)
- [Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service](https://mlsanity.com/paper/2104.01036/) (2021)
- [Forecasting Popularity of Videos using Social Media](https://mlsanity.com/paper/1403.5603/) (2014)
- [Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things](https://mlsanity.com/paper/1909.02445/) (2019)
- [Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks](https://mlsanity.com/paper/1812.05374/) (2018)
- [A Bayesian Poisson-Gaussian Process Model for Popularity Learning in Edge-Caching Networks](https://mlsanity.com/paper/1903.03065/) (2019)
- [Capacity-Aware Edge Caching in Fog Computing Networks](https://mlsanity.com/paper/2002.02173/) (2020)

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

- [Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks](https://mlsanity.com/paper/2210.05874/) (2022)
- [Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing](https://mlsanity.com/paper/2402.07366/) (2024)
- [CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2303.12097/) (2023)
- [Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network](https://mlsanity.com/paper/2405.17436/) (2024)

## Abstract (arXiv)

Nowadays wireless communication is rapidly reshaping entire industry sectors. In particular, mobile edge computing (MEC) as an enabling technology for industrial Internet of things (IIoT) brings powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significant lowers the response latency. To reap the benefit of proactive caching at the network edge, precise knowledge on the popularity pattern among the end devices is essential. However, the complex and dynamic nature of the content popularity over space and time as well as the data-privacy requirements in many IIoT scenarios pose tough challenges to its acquisition. In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT. The concepts of local and global popularities are introduced and the time-varying popularity of each user is modelled as a model-free Markov chain. On this basis, a novel unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the distributed popularity while achieve privacy preservation and unsupervised training. Simulations indicate that the proposed framework can enhance the prediction accuracy in terms of a reduced root-mean-squared error by up to 60.5%-68.7%. Additionally, manual labeling and violation of users' data privacy are both avoided.