论文标题
创作中的实时边缘智能：通过联合元学习的协作学习框架
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning
论文作者
论文摘要
网络边缘的许多物联网应用程序都需要实时的智能决策。但是，仅由于边缘设备的限制计算资源和有限的本地数据，因此通常无法实现实时边缘智能。为了应对这些挑战，我们提出了一个平台辅助的协作学习框架，其中首先通过联合的元学习方法对模型进行了培训，然后迅速适应了在目标边缘节点上学习新任务的新任务，仅使用几个样品。此外，我们研究了在节点相似性和目标边缘的适应性性能下，在轻度条件下，提出的联合元学习算法的收敛性。为了抵制元学习算法对可能的对抗性攻击的脆弱性，我们进一步提出了基于分布强劲优化的联合元学习算法的强大版本，并在轻度条件下建立了其收敛性。不同数据集上的实验证明了基于元学习的联合元学习框架的有效性。
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.
