Paper Info
Title
Cartel - A System for Collaborative Transfer Learning at the Edge.
Abstract
As Multi-access Edge Computing (MEC) and 5G technologies evolve, new applications are emerging with unprecedented capacity and real-time requirements. At the core of such applications there is a need for machine learning (ML) to create value from the data at the edge. Current ML systems transfer data from geo-distributed streams to a central datacenter for modeling. The model is then moved to the edge and used for inference or classification. These systems can be ineffective because they introduce significant demand for data movement and model transfer in the critical path of learning. Furthermore, a full model may not be needed at each edge location. An alternative is to train and update the models online at each edge with local data, in isolation from other edges. Still, this approach can worsen the accuracy of models due to reduced data availability, especially in the presence of local data shifts. In this paper we propose Cartel, a system for collaborative learning in edge clouds, that creates a model-sharing environment in which tailored models at each edge can quickly adapt to changes, and can be as robust and accurate as centralized models. Results show that Cartel adapts to workload changes 4 to 8x faster than isolated learning, and reduces model size, training time and total data transfer by 3x, 5.7x and ~1500x, respectively, when compared to centralized learning.
Year
DOI
Venue
2019
10.1145/3357223.3362708
SoCC '19: ACM Symposium on Cloud Computing Santa Cruz CA USA November, 2019
Keywords
Field
DocType
Mobile-access Edge Computing (MEC), distributed machine learning, collaborative learning, transfer learning
Engineering management,Computer science,Transfer of learning,Cartel,Electronic engineering
Conference
ISBN
Citations 
PageRank 
978-1-4503-6973-2
2
0.37
References 
Authors
0
4
Name
Order
Citations
PageRank
Harshit Daga120.71
Patrick K. Nicholson220.37
Ada Gavrilovska343944.89
Diego Lugones4359.77