@inproceedings{069124c3e05b422394603cac7a8e980d,
title = "Distributed Spectrum-Based Fault Localization",
abstract = "Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.",
author = "Avraham Natan and Roni Stern and Meir Kalech",
note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",
year = "2023",
month = jun,
day = "27",
language = "English",
series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",
publisher = "AAAI press",
pages = "6491--6498",
editor = "Brian Williams and Yiling Chen and Jennifer Neville",
booktitle = "AAAI-23 Technical Tracks 5",
}