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UID:submissions.supercomputing.org_SC24_sess750_ws_indis109@linklings.com
SUMMARY:Secure Collaborative Model Training with Dynamic Federated Learnin
 g in Multi-Domain Environments
DESCRIPTION:Anestis Dalgkitsis, Alexandros Koufakis, Jorrit Stutterheim, A
 leandro Mifsud, Priyanka Atwani, Leon Gommans, Cees de Laat, Chrysa Papagi
 anni, and Ana Oprescu (University of Amsterdam, Netherlands)\n\nAccording 
 to the European Union Aviation Safety Agency (EASA), AI-based algorithms, 
 combined with extensive fleet data, could enable early detection of potent
 ial engine failures, leading to proactive predictive maintenance in air tr
 avel. At a global level, the Independent Data Consortium for Aviation (IDC
 A) recognizes the potential of collaborative data sharing in the airline i
 ndustry. However, data ownership-related issues, such as privacy, intellec
 tual property, and regulatory compliance, pose significant obstacles to re
 alizing the vision of combining fleet data to improve predictive maintenan
 ce algorithms. In this paper, we use NASA’s Turbofan Jet Engine Dataset (N
 -CMAPSS) to demonstrate how airlines could leverage the power of Federated
  Learning (FL) and microservices, to collaboratively train a global Machin
 e-Learning (ML) model that can enable airline companies to utilize their d
 ata for predictive maintenance, while maintaining control.\n\nTag: Archite
 cture, Data-Intensive, Network, Performance Optimization, System Administr
 ation\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Akbar
  Kara (Ciena, SCinet); Anees Al-Najjar (Oak Ridge National Laboratory (ORN
 L), SCinet); and Nik Sultana (Illinois Institute of Technology, SCinet)\n\
 n
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