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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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MethodSlimmable Federated Reinforcement Learning for Energy-Efficient Proactive Caching
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Orchestrating Service Function Chains with Joint Resource Optimization in NFV Networks
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Self-citationIoT Data Security: An Integration of Blockchain and Federated Learning
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Perancangan Federated Learning Berbasis Homomorphic Encryption untuk Perangkat Internet of Things
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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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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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