  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.
