Federated Learning Based Proactive Content Caching in Edge Computing
Open accessAbstract
Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.
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FLchain can provide distributed AI solutions to support intelligent and secure content caching at the network edge.
“Due to the high data distribution of large-scale mobile edge networks, FLchain can provide distributed AI solutions to support intelligent and secure content caching at the network edge [70].”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [70]
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Qian, Z., Li, G., Qi, T., & Dai, C. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111062 · Cited by 8 · see citing works
Meta-reinforcement learning for edge caching in vehicular networks
Sakr, H. M., & Elsabrouty, M. M. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04583-z · Cited by 7 · see citing works
Efficient Transmission and Secure Sharing of Sensing data under Distributed ISAC Conditions
Mu, J., Jing, Z., Cui, Y., Jing, X., Zhou, Q., & Ouyang, W. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10183227 · Cited by 7 · see citing works
STAFL: Staleness-Tolerant Asynchronous Federated Learning on Non-iid Dataset
Zhu, F., Hao, J., Chen, Z., Zhao, Y., Chen, B., & Tan, X. (2022) · Electronics · doi:10.3390/electronics11030314 · Cited by 7 · see citing works
FED6G: Federated Chameleon Learning for Network Slice Management in Beyond 5G Systems
Thantharate, A. (2022) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon56893.2022.9946488 · Cited by 7 · see citing works
A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems
Picano, B., & Fantacci, R. (2023) · ACM Transactions on Internet of Things · doi:10.1145/3584705 · Cited by 7 · see citing works
Federated Learning With Heterogeneous Client Expectations: A Game Theory Approach
Shen, S., Liu, C., & Lim, T. J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3464488 · Cited by 6 · see citing works
A Federated Learning Framework for Multi-Parameter Optimization in Edge Computing
Soma, R., Sahoo, S. K., Amin, F., & Mishra, S. K. (2025) · International Conference on Intelligent Systems and Embedded Design (ISED) · doi:10.1109/ised67359.2025.11405143 · Cited by 6 · see citing works
A Survey of Edge Computing Resource Allocation Strategies Based on Federated Learning
Ni, S., He, Y., Chen, L., Wang, Y., & Yu, F. (2023) · International Conference on Networking and Network Applications · doi:10.1109/nana60121.2023.00027 · Cited by 3 · see citing works
Performance Evaluation of Federated Learning in Edge Computing Environment
Kotecha, P., Dhoka, T., Bhatia, J., Kumhar, M., Gupta, R., Tanwar, S., & Jadav, N. K. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.279 · Cited by 6 · see citing works
Privacy-preserving intelligent content-caching scheme in heterogeneous aerial access networks
Masood, A., Dao, N., & Cho, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122834 · Cited by 6 · see citing works
Tian, W., Chen, Y., Ke, F., & Song, H. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3501364 · Cited by 6 · see citing works
Federated learning based caching in fog computing for future smart cities
Sharma, S., & Gupta, N. (2020) · Internet Technology Letters · doi:10.1002/itl2.225 · Cited by 6 · see citing works
End-to-End Deep Learning Proactive Content Caching Framework
Bakr, E. M., Ben-Ammar, H., Eraqi, H. M., Aly, S. G., ElBatt, T., & Ghamri-Doudane, Y. M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001030 · Cited by 6 · see citing works
- Authors
- Yu, Z., Hu, J., Min, G., Lu, H., Zhao, Z., Wang, H., & Georgalas, N.
- Affiliations
- University of Exeter (GB)
- Fudan University (CN)
- University of Electronic Science and Technology of China (CN)
- BT Group (United Kingdom) (GB)
- Venue
- 2018 IEEE Global Communications Conference (GLOBECOM)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 1-6
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W2917116807
- Cited by
- 140
- References
- 15
- Citations by year
- 2025: 11 · 2024: 18 · 2023: 21 · 2022: 31 · 2021: 33 · 2020: 20 · 2019: 6
- FWCI
- 12.19
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Globecom (publishedVersion)
- Open Research Exeter (University of Exeter) (submittedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Figshare (submittedVersion)
- Funders
- National Natural Science Foundation of China (612652, PIRSES-GA-2013-612652, 61602095)
- Engineering and Physical Sciences Research Council (EP/M013936/2)
- National Key Research and Development Program of China (2017YFB1400102)
- 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 › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- proactive content caching, edge computing, federated learning, stacked autoencoder, hybrid filtering, FedAvg, user privacy, 5G, hierarchy, cache efficiency
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