The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
The MovieLens Datasets: History and Context
Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Maddah-Ali, M. A., & Niesen, U. (2014) · IEEE Transactions on Information Theory · doi:10.1109/tit.2014.2306938 · Cited by 1,726
Living on the edge: The role of proactive caching in 5G wireless networks
Baştuǧ, E., Bennis, M., & Debbah, M. A. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6871674 · Cited by 1,214
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
Wu, Y., DuBois, C., Zheng, A. X., & Ester, M. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835837 · Cited by 1,025
Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution
Golrezaei, N., Molisch, A. F., Dimakis, A. G., & Caire, G. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6495773 · Cited by 686
Yu, X., Wu, X., Luo, C., & Ren, P. (2017) · GIScience & Remote Sensing · doi:10.1080/15481603.2017.1323377 · Cited by 286
Context-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
Popularity-driven content caching
Li, S., Xu, J., van der Schaar, M., & Li, W. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524381 · Cited by 165
Research works printed but not linked: 2
- ng, 2011, Sparse autoencoder, Cs294a lecture notes, 72, 1
- 2017, Cisco visual networking index: forecast and methodology, 2016–2021, white paper, Cisco visualizations
Cited by
Sorted by citations
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
“Instead of using a DRL approach, the authors in [176] propose the use of an FL based stacked autoencoder learning model, i.”
MethodFederated 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
“As shown in [66], FL is very useful to build proactive data caching schemes in edge computing without the need for direct access to user data to predict the most popular files for caching.”
BackgroundMethodInfluentialA Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
“One related area with FL is edge computing [44], [127], [140], [195], [201], where the parties are edge devices.”
BackgroundBlockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942190 · Cited by 1,245
“Based on a hierarchical architecture in which the server aggregates the users’ training updates, the authors in [22] proposed a federated learning based proactive content caching scheme.”
MethodFederated 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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
