Optimizing Aggregation Frequency for Hierarchical Model Training in Heterogeneous Edge Computing

Article Computer Science, Theory & Methods

Accelerating Gossip-Based Deep Learning in Heterogeneous Edge Computing Platforms

Rui Han et al.

Summary: Research shows that with the exponential growth of data generated by edge computing, the decentralized and Gossip-based training of deep learning models is gaining momentum. The EdgeGossip framework is designed to reduce the performance variation among heterogeneous edge platforms during training and achieve best possible model accuracy quickly. Implementing EdgeGossip based on popular Gossip algorithms has demonstrated an average reduction of model training time by 2.70 times with only 0.78% accuracy loss.

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS (2021)

Add to Collection

Article Computer Science, Information Systems

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

Lei Yang et al.

Summary: The study introduces a novel decentralized model learning approach E-Tree, utilizing a well-designed tree structure on edge devices to optimize training convergency and model accuracy. Evaluation results show that E-Tree outperforms benchmark approaches under Non-i.i.d. data in terms of model accuracy and convergency.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Proceedings Paper Computer Science, Software Engineering