The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.
Content Popularity Prediction in Fog Radio Access Networks: A Federated Learning Based Approach
Abstract
In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users' context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.
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FL is a viable solution to estimate file popularity without disclosing sensitive user information.
“FL comes as a viable solution to estimate the file popularity without disclosing sensitive user information and user preference.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [73]
The popularity prediction of proactive edge caching mitigates traffic load and enhances user experience.
“In fact, the popularity prediction of proactive edge caching at BSs can mitigate the traffic load and enhance user experience, by selecting the most popular and important contents in the offloaded data package.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [73]
Citations
Cites
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Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938
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“Traditional caching policies, such as least recently used (LRU), least frequently used (LFU), are widely used in wired networks, but their efficiency is limited since content popularity is not considered [2].”
BackgroundContext-Aware Proactive Content Caching With Service Differentiation in Wireless Networks
Muller, S., Atan, O., van der Schaar, M., & Klein, A. (2016) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2636139 · Cited by 282
User Preference Learning-Based Edge Caching for Fog Radio Access Network
Jiang, Y., Ma, M., Bennis, M., Zheng, F., & You, X. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2880482 · Cited by 180
Self-citationContent Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDN
Liu, W., Zhang, J., Liang, Z., Peng, L., & Cai, J. (2017) · IEEE Access · doi:10.1109/access.2017.2781716 · Cited by 142
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Trend-Aware Video Caching Through Online Learning
Li, S., Xu, J., van der Schaar, M., & Li, W. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2596042 · Cited by 98
Wireless Network Intelligence at the Edge
Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2941458 · Cited by 92
Self-citationPopularity prediction of images and videos on Instagram
Zohourian, A., Sajedi, H., & Yavary, A. (2018) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2018.8387246 · Cited by 41
ARMA based popularity prediction for caching in Content Delivery Networks
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Content Popularity Prediction via Deep Learning in Cache-Enabled Fog Radio Access Networks
Feng, H., Jiang, Y., Niyato, D. T., Zheng, F., & You, X. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013376 · Cited by 30
Self-citationLiu, Y., Zhi, T., Xi, H., Duan, X., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2901525 · Cited by 27
Jing, W., Wen, X., Lu, Z., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2918334 · Cited by 24
Clustered Popularity Prediction for Content Caching
Chen, Q., Wang, W., & Zhang, Z. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761450 · Cited by 10
A Bayesian Poisson–Gaussian Process Model for Popularity Learning in Edge-Caching Networks
Mehrizi, S., Tsakmalis, A., Chatzinotas, S., & Ottersten, B. (2019) · IEEE Access · doi:10.1109/access.2019.2927494
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Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503
“Meanwhile, the authors in [72], [73] pay attention to content popularity prediction with FL.”
BackgroundFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks
Νομικός, Ν., Zoupanos, S., Charalambous, T., & Krikidis, I. (2022) · IEEE Access · doi:10.1109/access.2022.3140719 · Cited by 62
Li, D., Zhang, H., Li, T., Ding, H., & Yuan, D. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3249756 · Cited by 30
“However, these works in [27], [28], [29], and [30] only did a simple average aggregation of the local model parameters, which was not effective since different devices might have different model performances.”
BackgroundMethodMECC: A Mobile Edge Collaborative Caching Framework Empowered by Deep Reinforcement Learning
Xu, S., Liu, X., Guo, S., Qiu, X., & Meng, L. (2021) · IEEE Network · doi:10.1109/mnet.011.2000663 · Cited by 25
Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks
Huang, X., Chen, Z., Chen, Q., & Zhang, J. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.04.022 · Cited by 25
Multi-Agent Caching Strategy for Spatial-Temporal Popularity in IoV
He, P., Li, C., Cui, Y., Wang, R., & Wu, D. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3277191 · Cited by 24
“In [15], based on the user preference learning, adaptive context partition, and federated learning, the authors proposed a content popularity prediction strategy to reduce the complexity of prediction, which could obtain accurate content popularity when the contents have no statistical data in…”
BackgroundFederated Learning-Based Content Popularity Prediction in Fog Radio Access Networks
Jiang, Y., Wu, Y., Zheng, F., Bennis, M., & You, X. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3124586 · Cited by 23
“Given enough samples, the likelihood function of un can be expressed as follows [26]:”
BackgroundSelf-citationOptimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Zhang, X., Zhou, Y., Wu, D., Hu, M., Zheng, X., Chen, M., & Guo, S. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3147240 · Cited by 17
“Wang et al. developed a transfer learning algorithm to model the prominence of video content from social streams [44], while Roy et al. proposed a novel context-aware popularity prediction policy based on federated learning [45].”
BackgroundPartially Collaborative Edge Caching Based on Federated Deep Reinforcement Learning
Lei, M., Li, Q., Ge, X., & Pandharipande, A. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3206876 · Cited by 11
“The CS, on the other hand, is responsible for coordinating the collaborative caching among FAPs [17].”
BackgroundInfluentialQaiser, F., Harthy, K. S. A., Hussain, M., Frnda, J., Amin, R., Gantassi, R., & Zakaria, M. D. (2025) · Engineering Reports · doi:10.1002/eng2.70005 · Cited by 12
Improved AFSA-Based Energy-Aware Content Caching Strategy for UAV-Assisted VEC
Long, K., Li, C., Jiang, K., & Wan, S. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3444949 · Cited by 9
“…( F c ) to approximate the relationship between the feature vector F c of the content and the vehicle preference vector H v , which can be expressed as follows [25]: Weuse y v,c todenotewhetherthevehicle v hasrequestedcontent c , and y v,c = 1 to denote that the vehicle v has requested content c .”
Tao, Y., Jiang, Y., Zheng, F., Wang, Z., Zhu, P., Tao, M., Niyato, D. T., & You, X. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3229679 · Cited by 10
“In [15] and [16], federated learning (FL) was utilized to obtain the context-aware popularity prediction model.”
MethodSelf-citationThomas, A. K., Reddy, C. L., Suman, S. K., Tamilselvi, S., Devarasu, N., & Arulananth, T. S. (2024) · International Conference on Advancement in Electronics & Communication Engineering (AECE) · doi:10.1109/aece62803.2024.10911795 · Cited by 8
“Federated and reinforcement learning can improve money, health, and reading judgments [15-17].”
RetractedA Privacy-Protected Popularity Prediction Scheme for Content Caching Based on Federated Learning
Wang, K., & Deng, N. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3179413 · Cited by 8
“The scheme in [13] had the same problem, in which users’ preferences were sent to their associated fog access point at risk of privacy leakage.”
BackgroundCache-Enabled Federated Learning Systems
Liu, Y., Su, L., Joe‐Wong, C., Ioannidis, S., Yeh, E. M., & Siew, M. (2023) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3565287.3610264 · Cited by 8
Caching Strategy Based on Content Popularity Prediction Using Federated Learning for F-RAN
Jiang, F., Cheng, W., Gao, Y., & Sun, C. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/icccworkshops52231.2021.9538910 · Cited by 7
Content Popularity Prediction in Fog-RANs: A Bayesian Learning Approach
Tao, Y., Jiang, Y., Zheng, F., Bennis, M., & You, X. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685947 · Cited by 6
“In [7], the interference among mobile devices in federated learning remains an open issue.”
BackgroundMethodSelf-citationCollaborative Caching in Edge Computing via Federated Learning and Deep Reinforcement Learning
Wang, Y., & Chen, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/7212984 · Cited by 4
“Federated learning is a distributed machine learning framework that can effectively solve this problem [16, 17].”
BackgroundNalbant, K. G., Almutairi, S., Alshehri, A. H., Kemal, H., Alsuhibany, S. A., & Choi, B. J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305092 · Cited by 4
Wang, J., Li, J., Liu, C., Yan, P., & Li, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-91752-z · Cited by 4
“To achieve the interaction of two models, Wu et al. 30 developed a multi-model fusion framework based on generative adversarial learning.”
Content Popularity Prediction Based on Integrated Features and Federated Learning
Xiong, Y., Jin, H., Feng, T., Ruijuan, J., Zhang, Q., & Zhao, C. (2021) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/ic-nidc54101.2021.9660437 · Cited by 4
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
Wang, Z., Jiang, Y., Zheng, F., Bennis, M., & You, X. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839007 · Cited by 4
“In [5] and [6], the edge caching scenario is considered in a specific region, where all F-APs are thought to have the same content popularity.”
BackgroundSelf-citationOnline Multicast Traffic Engineering for Multi-View Videos With View Synthesis in SDN
Chiang, S.-H., Wang, C.-H., Yang, D.-N., Liao, W., & Chen, W.-T. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3366166 · Cited by 1
“Following [48] and [49], we calculate the mean squared error (MSE) of popularity and adjust the prediction interval K (defined before (1)) when the MSE is larger than threshold p .”
MethodDevice Selection for Resource-Efficient Edge Caching in a Federated Learning Framework
Kushwaha, D., Narkhede, M., Limaye, A., Pol, N., & Hegde, R. M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890488 · Cited by 1
A survey on social media popularity prediction: concepts, technologies, applications, and challenges
Liu, Y., Lei, Q., Liu, Z., Zhao, M., Xu, Z., & Shi, L. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00909-x · Cited by 1
Content Popularity Prediction with Integrated Features in UAV-Assisted Wireless Caching Networks
Shi, K., Li, B., Wang, Y., Ran, B., Kong, P., Huang, X., Ma, D., & Liang, F. (2022) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct56141.2022.10073287 · Cited by 1
“(8) Follow The (Proximally) Regularized Leader (FTRL-Proximal) algorithm is adopted to learn the user preference [7].”
MethodPopularity Prediction based Cache Update Strategy with Age of Information
Yao, T., Jiang, F., & Chen, Z. (2024) · doi:10.1109/icnlp60986.2024.10692523
“1) FLBA algorithm [11].”
BackgroundDynamic Content Popularity Prediction Based on Spatial-Temporal Feature in Vehicular Networks
Ying, S., Zang, Y., Wang, T., Chen, Y., & Feng, C. (2025) · Intelligent and Converged Networks · doi:10.23919/icn.2025.0029
Metadata
- Authors
- Wu, Y., Jiang, Y., Bennis, M., Zheng, F., Gao, X., & You, X.
- Affiliations
- Southeast University (CN)
- University of Oulu (FI)
- Venue
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W3046032603
- Cited by
- 29
- References
- 17
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 7
- FWCI
- 3.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- content popularity prediction, fog radio access networks, federated learning, user preference learning, user clustering, SVRG, model integration, cache hit rate, edge caching
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