Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning

Understand

In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge.

• However, the constrained resources and limited local data amount pose significant challenges to the development of edge AI.
• To overcome these challenges, we explore continual edge learning capable of leveraging the knowledge transfer from previous tasks.
• Aiming to achieve fast and continual edge learning, we propose a platform-aided federated meta-learning architecture where edge nodes collaboratively learn a meta-model, aided by the knowledge transfer from prior tasks.