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

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.

References in corpus (1)

• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

Cited by in corpus (6)

• The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
• Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective
• Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
• State-of-the-art Techniques in Deep Edge Intelligence
• System Identification via Meta-Learning in Linear Time-Varying Environments
• Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence