Robust Decentralized Online Learning against Malicious Data Generators and Dynamic Feedback Delays with Application to Traffic Classification

Yupeng Li*, Dacheng Wen, Mengjia Xia

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Motivated by the real-world application of traffic classification at the network edge, we study the problem of robust decentralized online learning against malicious data generators that can manipulate their data features with an aim to gain preferred classification outcomes. Multiple agents cooperatively learn classification models to make online decisions. They periodically exchange their models, e.g., traffic classification models, between neighbors in a decentralized network and update local model parameters on the fly based on the models they have access to and feedback on the observed local data samples that are dynamically delayed. In this work, we propose two decentralized online learning algorithms, RDOC-O and RDOC-C, respectively against ordinary malicious and clairvoyant malicious data generators. Our theoretical performance analysis shows that the two algorithms have provable sub-linear individual regret bounds under mild conditions. To validate our analysis, extensive performance evaluations are conducted in the application of network traffic classification using two real-world data traces. Our results show that the two proposed algorithms compare favorably with an optimal offline classification model in the presence of malicious data generators, and they can achieve a steady-state F1 score of around 0.85, which validates their effectiveness and makes them appealing in practice.

Original languageEnglish
Title of host publication2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
PublisherIEEE
Pages465-473
Number of pages9
ISBN (Electronic)9798350300529
ISBN (Print)9798350300536
DOIs
Publication statusPublished - 11 Sept 2023
Event20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023 - Madrid, Spain
Duration: 11 Sept 202314 Sept 2023
https://ieeexplore.ieee.org/xpl/conhome/10287388/proceeding (Conference proceedings)

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
Volume2023-September
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
Country/TerritorySpain
CityMadrid
Period11/09/2314/09/23
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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