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Federated learning

Keywords:
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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Quantum Technology

Background:

  • Federated learning (FL) systems aggregate model updates from decentralized devices.
  • Byzantine-resilient aggregation algorithms are crucial for FL security.
  • Quantum sensing is explored for advanced cybersecurity applications.

Purpose of the Study:

  • To identify vulnerabilities in FL aggregation algorithms and assess quantum sensing for cybersecurity.
  • To propose resilient FL solutions and evaluate quantum security feasibility.

Main Methods:

  • Empirical analysis of the Krum aggregation algorithm in high-dimensional FL.
  • Simulation-based feasibility study of quantum-enhanced cybersecurity.
  • Comparative analysis of five alternative FL aggregation algorithms.

Main Results:

  • Krum algorithm exhibits critical vulnerability in high-dimensional FL, causing significant accuracy degradation.
  • FLTrust shows resilience but requires trusted infrastructure.
  • Quantum sensing for cybersecurity faces major barriers: extreme EMI, high costs, lack of correlation, and scalability issues.

Conclusions:

  • Immediate implications for production FL systems regarding algorithm choice.
  • Quantum sensing for cybersecurity is currently infeasible due to fundamental deployment challenges.
  • Further research needed for physical validation of quantum sensing, addressing EMI and calibration.