Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning

• URL: http://arxiv.org/abs/2001.03229v2
• Date: Fri, 8 May 2020 23:54:45 GMT
• Title: Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning
• Authors: Sen Lin, Guang Yang and Junshan Zhang
• Abstract summary: IoT applications at the network edge demand intelligent decisions in a real-time manner. We propose a platform-aided collaborative learning framework where a model is first trained across a set of source edge nodes. We then adapt the model to learn a new task at the target edge node, using a few samples only.
• Score: 24.00507627945666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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