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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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“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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“In [38], the authors propose a federated learning-based proactive content caching (FPCC) scheme which is based on a hierarchical architecture where the server aggregates user-side updates to construct a global model.”
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“A proactive content caching scheme [22] outperforms other learning-based caching algorithms without gathering private data based on a hierarchical FL architecture.”
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URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence
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Resource Scheduling of Time-Sensitive Services for B5G/6G Connected Automated Vehicles
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Distributed Edge System Orchestration for Web-based Mobile Augmented Reality Services
Ren, P., Liu, L., Qiao, X., & Chen, J. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3190375 · Cited by 14
“…caching schemes, for example, graph-based solu-tion [31], deep reinforcement learning-based solution [32], and federated learning-based solution [33]; while many solutions for the handover problem in the edge system are starting from the social network and wireless communication fields to…”
Qian, Z., Feng, Y., Dai, C., Li, G., & Li, G. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111795 · Cited by 13
Mobility-Aware Federated Learning-Based Proactive UAVs Placement in Emerging Cellular Networks
Manzoor, S., Shakir, M. Z., Hasna, M. O., & Qaraqe, K. A. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3439289 · Cited by 12
Qaiser, 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
FedCo: A Federated Learning Controller for Content Management in Multi-party Edge Systems
Balasubramanian, V., Aloqaily, M., & Martin Reisslein, B. (2021) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn52240.2021.9522153 · Cited by 12
Content-Aware Caching at the Mobile Edge Network Using Federated Learning
Lekharu, A., Samanta, A., Sur, A., & Patra, M. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3402166 · Cited by 11
“Federated Learning [11], [12], [13], [14], [15], [16], [17], [18], [19] has been introduced recently to improve the caching performance at the edge server and efficiently offload the computation task from the central server to the end-users.”
Odeh, J. O., Yang, X., Nwakanma, C. I., & Dhelim, S. (2024) · Electronics · doi:10.3390/electronics13091610 · Cited by 11
Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs
Xiao, T., Cui, T., Islam, S. M. R., & Chen, Q. (2020) · Sensors · doi:10.3390/s21010215 · Cited by 10
“However, most existing caching schemes are designed for highly controlled environments where users need to upload local private data to a central server, which may pose privacy and security risks [7].”
BackgroundMethodAn Energy-Aware Multi-Criteria Federated Learning Model for Edge Computing
Al-Saedi, A. A., Casalicchio, E., & Boeva, V. (2021) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud49777.2021.00027 · Cited by 11
Messaoud, S., Bouaafia, S., Bradai, A., Hajjaji, M. A., Mtibaa, A., & Atri, M. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.102472 · Cited by 9
Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G
Karachalios, O. A., Zafeiropoulos, A., Kontovasilis, K. P., & Papavassiliou, S. (2023) · Electronics · doi:10.3390/electronics12183761 · Cited by 12
Thomas, 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
RetractedCao, T., Zhang, N., Wang, X., & Huang, J. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3571902 · Cited by 8
“Subsequently, the cosine similarity between active vehicles V and V is calculated as [34]: where H r i is the joint matrix formed by ˆ R r i and P r i ,In the combination matrix, H ri ( ,”
Achieving Optimal Cache Utility in Constrained Wireless Networks through Federated Learning
Chilukuri, S., & Pesch, D. (2020) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom49955.2020.00053 · Cited by 8
“In [21], the authors apply FL for proactive content caching in the network, where content popularity over different geographical regions can be leveraged to cache data proactively.”
BackgroundA 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 cache hit ratio of the PFLC is similar to that of the FPCC [14] and Oracle, which means that the PFLC can achieve high cache hit ratio and accurately predict the preference of the user group.”
MethodInfluentialClassical and quantum compression for edge computing: the ubiquitous data dimensionality reduction
Bagherian, M., Chehade, S., Whitney, B., & Passian, A. (2023) · Computing · doi:10.1007/s00607-023-01154-0 · Cited by 10
“…Clinical Disease history, treatment [23] Biomedical Florescence images of circulating tumor cells, MRI, PET [14] Demographics Race, sex, ethnicity [24] Sensitive Financial, defense related, personal data [25] Scientific Microscope images and spectra; Nuclear and particle data [26, 27] The nature…”
BackgroundQian, Z., Li, G., Qi, T., & Dai, C. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111062 · Cited by 8
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
“In this same context, federated learning has been introduced to proactive caching in Yu et al. (2018), where the popularity of the content is updated hierarchically without the need to access the individual user access data.”
BackgroundEfficient 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
“[9] proposed an active content caching scheme based on FL in mobile edge computing.”
MethodSTAFL: 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
“Federated learning can address the concerns of a response time requirement, battery life constraint, bandwidth cost-saving, and data safety and privacy [3–6].”
BackgroundFED6G: 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
“A federated learning-based content caching algorithm considers user privacy, while content caching on a real-world dataset is studied by Z. Yu et al. in [8].”
MethodA 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
“Finally, we would like to stress that the reference FL architecture framework considered is the same of [47], consisting of stacked autoencoder based on hybrid iltering to forecast the popularity vector � of VMs more accurately.”
MethodFederated 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
“In addition, FL is able to facilitate IoT data of-floading and caching services in many applied domains, such as mobile edge computing [34], [35], [36].”
BackgroundA 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 11
“Yang et al. developed dynamic resource allocation algorithms to lower energy consumption without compromising model accuracy, while Wang et al. proposed energy-efficient client selection based on battery levels and compute capability [9].”
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
“Yu et al. propose to use a federated learning-based active content caching scheme to predict the popularity of content to optimize caching [26].”
MethodPerformance 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 8
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
Tian, W., Chen, Y., Ke, F., & Song, H. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3501364 · Cited by 6
“Federated Learning (FL) is a distributed learning framework that allows trainers to training locally and use model updates to communicate between vehicles and edge servers, effectively addressing privacy concerns [13], [14].”
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
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
“The authors in [4] introduce a Federated learning-based Proactive Content”
Background
Metadata
- 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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