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Edge Cloud Computing and Federated–Split Learning in Internet of Things

Author

Listed:
  • Qiang Duan

    (Information Sciences and Technology Department, Pennsylvania State University, Abington, PA 19001, USA)

  • Zhihui Lu

    (School of Computer Science, Fudan University, Shanghai 200437, China)

Abstract

The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data [...]

Suggested Citation

  • Qiang Duan & Zhihui Lu, 2024. "Edge Cloud Computing and Federated–Split Learning in Internet of Things," Future Internet, MDPI, vol. 16(7), pages 1-4, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:7:p:227-:d:1424294
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    References listed on IDEAS

    as
    1. Rezak Aziz & Soumya Banerjee & Samia Bouzefrane & Thinh Le Vinh, 2023. "Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm," Future Internet, MDPI, vol. 15(9), pages 1-25, September.
    2. Ahmed A. Al-Saedi & Veselka Boeva & Emiliano Casalicchio, 2022. "FedCO: Communication-Efficient Federated Learning via Clustering Optimization," Future Internet, MDPI, vol. 14(12), pages 1-27, December.
    3. Lijun Zu & Hongyi Li & Liang Zhang & Zhihui Lu & Jiawei Ye & Xiaoxia Zhao & Shijing Hu, 2023. "E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios," Future Internet, MDPI, vol. 15(9), pages 1-17, August.
    4. Fotis Nikolaidis & Moysis Symeonides & Demetris Trihinas, 2023. "Towards Efficient Resource Allocation for Federated Learning in Virtualized Managed Environments," Future Internet, MDPI, vol. 15(8), pages 1-26, July.
    Full references (including those not matched with items on IDEAS)

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