  Many IoT applications at the network edge demand intelligent decisions in a
real-time manner. The edge device alone, however, often cannot achieve
real-time edge intelligence due to its constrained computing resources and
limited local data. To tackle these challenges, we propose a platform-aided
collaborative learning framework where a model is first trained across a set of
source edge nodes by a federated meta-learning approach, and then it is rapidly
adapted to learn a new task at the target edge node, using a few samples only.
Further, we investigate the convergence of the proposed federated meta-learning
algorithm under mild conditions on node similarity and the adaptation
performance at the target edge. To combat against the vulnerability of
meta-learning algorithms to possible adversarial attacks, we further propose a
robust version of the federated meta-learning algorithm based on
distributionally robust optimization, and establish its convergence under mild
conditions. Experiments on different datasets demonstrate the effectiveness of
the proposed Federated Meta-Learning based framework.
