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

Abstract

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.

arXiv comment17 pages, 15 figures, accepted for publication in IEEE INTERNET OF THINGS JOURNAL

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• Multivariate LSTM-FCNs for Time Series Classification
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
• Big Data Caching for Networking: Moving from Cloud to Edge
• Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities
• Predicting popularity of online videos using Support Vector Regression
• Forecasting Popularity of Videos using Social Media
• Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things
• Revocable Federated Learning: A Benchmark of Federated Forest

Cited by in corpus (1)

• A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges