Applications of Federated Learning in IoT for Hyper Personalisation
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Abstract (arXiv)
Billions of IoT devices are being deployed, taking advantage of faster internet, and the opportunity to access more endpoints. Vast quantities of data are being generated constantly by these devices but are not effectively being utilised. Using FL training machine learning models over these multiple clients without having to bring it to a central server. We explore how to use such a model to implement ultra levels of personalization unlike before
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 28 Sep 2023.
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References · 10
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
- 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
- 2021Federated Learning: Opportunities and Challengescited by 84
- 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
- 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 50
- 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
- 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
- 2021Personalised Federated Learning: A Combinational Approachcited by 3
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