E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI

TL;DR

E-Tree Learning introduces a decentralized, tree-structured framework for edge AI model training that enhances convergence and accuracy over traditional federated approaches, especially with non-IID data.

Contribution

The paper presents a novel decentralized model learning framework, E-Tree, with an optimized tree structure and a device clustering algorithm for improved edge AI training.

Findings

E-Tree outperforms federated and Gossip learning in accuracy.

E-Tree achieves faster convergence.

The clustering algorithm effectively considers data distribution and network distance.

Abstract

Traditionally, AI models are trained on the central cloud with data collected from end devices. This leads to high communication cost, long response time and privacy concerns. Recently Edge empowered AI, namely Edge AI, has been proposed to support AI model learning and deployment at the network edge closer to the data sources. Existing research including federated learning adopts a centralized architecture for model learning where a central server aggregates the model updates from the clients/workers. The centralized architecture has drawbacks such as performance bottleneck, poor scalability and single point of failure. In this paper, we propose a novel decentralized model learning approach, namely E-Tree, which makes use of a well-designed tree structure imposed on the edge devices. The tree structure and the locations and orders of aggregation on the tree are optimally designed to improve the training convergency and model accuracy. In particular, we design an efficient device clustering algorithm, named by KMA, for E-Tree by taking into account the data distribution on the devices as well as the the network distance. Evaluation results show E-Tree significantly outperforms the benchmark approaches such as federated learning and Gossip learning under NonIID data in terms of model accuracy and convergency.