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As noted before, it is anticipated that a high percentage of IoT data will be stored and processed locally. However, because many AI applications typically require high computational power that greatly outweighs the capacity of resource- and energy-constrained IoT devices, it is highly challenging for a single edge node alone to achieve real-time edge intelligence, which points to the need of collaborative learning that is capable of leveraging the knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.

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Edge Intelligence via Federated Meta-Learning

  • 章节

  • 发表日期：

    2025年

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  • Sen Lin,
  • Zhi Zhou,
  • Zhaofeng Zhang,
  • Xu Chen,
  • Junshan Zhang

摘要

As noted before, it is anticipated that a high percentage of IoT data will be stored and processed locally. However, because many AI applications typically require high computational power that greatly outweighs the capacity of resource- and energy-constrained IoT devices, it is highly challenging for a single edge node alone to achieve real-time edge intelligence, which points to the need of collaborative learning that is capable of leveraging the knowledge transferred from other edge nodes or the cloud. In this chapter, we focus on collaborative learning across edge nodes, and turn our attention to collaborative learning between the edge and the cloud in next chapter, aiming to fully leverage the potentially valuable knowledge transfer from the cloud.