DOAJ Open Access 2025

CacheHitPredictor: A Federated Learning Approach to Edge Cache Hit Prediction in URLLC Systems

Taofeek Olaidey Salaudeen Nur Idora Abdul Razak Syahrul Afzal Bin Che Abdullah Supaporn Chairungsee

Abstrak

Efficient edge caching is vital for meeting the stringent latency and reliability demands of Ultra-Reliable Low Latency Communication (URLLC) in 5G and beyond. This paper proposes CacheHitPredictor, a federated learning-based framework for predictive edge caching that preserves data privacy while handling client heterogeneity and dynamic user behavior. A URLLC scenario was simulated using CloudSimPlus to generate realistic user request traces. The resulting dataset was used to train a federated neural network model via the Flower framework, with varying Dirichlet factors to evaluate data heterogeneity. The trained model was deployed via a Flask REST API and integrated with the CloudSimPlus simulation to enable online cache decision-making. Experimental results demonstrate that the proposed model performs on par with LFU under skewed workloads, underscoring the practical potential of federated learning to enable adaptive and efficient caching in URLLC edge environments.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering

Penulis (4)

Taofeek Olaidey Salaudeen

Nur Idora Abdul Razak

Syahrul Afzal Bin Che Abdullah

Supaporn Chairungsee

Format Sitasi

APA MLA BibTeX

Salaudeen, T.O., Razak, N.I.A., Abdullah, S.A.B.C., Chairungsee, S. (2025). CacheHitPredictor: A Federated Learning Approach to Edge Cache Hit Prediction in URLLC Systems. https://doi.org/10.1109/ACCESS.2025.3628825

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.1109/ACCESS.2025.3628825
Akses: Open Access ✓