research-article

Cartel: A System for Collaborative Transfer Learning at the Edge

Authors:

Patrick K. Nicholson,

Ada Gavrilovska,

Diego LugonesAuthors Info & Claims

SoCC '19: Proceedings of the ACM Symposium on Cloud Computing

Pages 25 - 37

https://doi.org/10.1145/3357223.3362708

Published: 20 November 2019 Publication History

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.

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Index Terms

Cartel: A System for Collaborative Transfer Learning at the Edge
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. n-tier architectures
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
    2. Learning settings
      1. Online learning settings

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SoCC '19: Proceedings of the ACM Symposium on Cloud Computing

November 2019

503 pages

ISBN:9781450369732

DOI:10.1145/3357223

Copyright © 2019 ACM.

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Published: 20 November 2019

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SoCC '19: ACM Symposium on Cloud Computing

November 20 - 23, 2019

CA, Santa Cruz, USA

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SoCC '19 Paper Acceptance Rate 39 of 157 submissions, 25%;

Overall Acceptance Rate 169 of 722 submissions, 23%

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