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

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 ha...

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Ngā kaituhi matua:	Taofeek Olaidey Salaudeen, Nur Idora Abdul Razak, Syahrul Afzal Bin Che Abdullah, Supaporn Chairungsee
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2025-01-01
Rangatū:	IEEE Access
Ngā marau:	Mobile edge caching (MEC) 5G URLLC federated learning data heterogeneity cache hit rate
Urunga tuihono:	https://ieeexplore.ieee.org/document/11224831/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

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

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