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Computer Science > Machine Learning

arXiv:2010.05888 (cs)
[Submitted on 12 Oct 2020 (v1), last revised 31 Dec 2020 (this version, v2)]

Title:Garfield: System Support for Byzantine Machine Learning

Authors:Rachid Guerraoui, Arsany Guirguis, Jérémy Max Plassmann, Anton Alexandre Ragot, Sébastien Rouault
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Abstract:We present Garfield, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine-resilient. Garfield relies on a novel object-oriented design, reducing the coding effort, and addressing the vulnerability of the shared-graph architecture followed by classical ML frameworks. Garfield encompasses various communication patterns and supports computations on CPUs and GPUs, allowing addressing the general question of the very practical cost of Byzantine resilience in SGD-based ML applications. We report on the usage of Garfield on three main ML architectures: (a) a single server with multiple workers, (b) several servers and workers, and (c) peer-to-peer settings. Using Garfield, we highlight several interesting facts about the cost of Byzantine resilience. In particular, (a) Byzantine resilience, unlike crash resilience, induces an accuracy loss, (b) the throughput overhead comes more from communication than from robust aggregation, and (c) tolerating Byzantine servers costs more than tolerating Byzantine workers.
Comments: 31 pages; 16 figures; 2 tables
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2010.05888 [cs.LG]
  (or arXiv:2010.05888v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2010.05888

Submission history

From: Arsany Guirguis [view email]
[v1] Mon, 12 Oct 2020 17:36:19 UTC (797 KB)
[v2] Thu, 31 Dec 2020 13:45:15 UTC (6,735 KB)
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El-Mahdi El-Mhamdi
Rachid Guerraoui
Arsany Guirguis
Sébastien Rouault
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