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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges.

Guanming Bao1, Ping Guo1

  • 1School of Computer Science, Nanjing University of Information Science and Technology, Ningliu Road, 210044 Nanjing, China.

Journal of Cloud Computing (Heidelberg, Germany)
|December 20, 2022
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Federated learning in cloud-edge architecture combines edge computing and federated learning for enhanced data processing and privacy. This research explores its technologies, challenges, and applications, guiding future development.

Keywords:
Cloud-edge collaborative computingFederated learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • The proliferation of edge data necessitates advanced processing capabilities beyond traditional cloud computing.
  • Growing public demand for data privacy has spurred the development of federated learning to address centralized machine learning security concerns.
  • The integration of federated learning within cloud-edge collaborative architectures is emerging as a significant future cyber infrastructure.

Purpose of the Study:

  • To address the nascent research in deploying federated learning within cloud-edge collaborative architectures.
  • To provide a comprehensive overview of critical technologies, challenges, and applications in this domain.
  • To offer guidance for future research directions in federated learning for cloud-edge environments.

Main Methods:

  • Literature review and synthesis of existing research on cloud-edge computing and federated learning.
  • Analysis of key technological components enabling federated learning in a distributed cloud-edge setting.
  • Identification and categorization of challenges inherent in this integrated architecture.
  • Exploration of potential application scenarios and use cases.

Main Results:

  • The study consolidates current understanding of federated learning deployment in cloud-edge architectures.
  • Key technological enablers such as distributed data management, secure aggregation, and edge intelligence are highlighted.
  • Significant challenges including communication overhead, resource constraints at the edge, and privacy-preserving mechanisms are detailed.
  • A range of applications, from IoT analytics to personalized services, are identified.

Conclusions:

  • Federated learning in cloud-edge collaborative architectures presents a promising paradigm for scalable, privacy-preserving data analysis.
  • Overcoming identified challenges is crucial for realizing the full potential of this integrated approach.
  • Further research is needed to optimize performance, security, and efficiency for widespread adoption.