论文标题
在物联网网络中用于联合边缘学习的无监督数据拆分方案
Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks
论文作者
论文摘要
联合边缘学习（Feel）是一种有希望的分布式学习技术，旨在培训共享的全球模型，同时降低沟通成本并促进用户的隐私。但是，由于训练的性质，训练过程可能会大大占据很长时间，这会导致更高的能耗，因此会影响模型收敛。为了解决此问题，我们提出了一个以数据为导向的联合边缘学习计划，该计划倾向于根据质量数据和能量选择合适的参与节点。首先，我们设计了一个无监督的数据感知分裂方案，该方案将节点的本地数据分配为用于培训的各种样本。我们将相似性指数纳入选择质量数据以增强训练性能。然后，我们提出了一种启发式参与节点选择方案，以最大程度地减少通信和计算能量消耗以及沟通量的数量。获得的结果表明，所提出的方案在能耗和通信回合的数量方面大大胜过香草的感觉。
Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a data-driven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node's local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds.
