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IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.

Arwa Badhib1, Suhair Alshehri1, Asma Cherif1,2

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

Federated Learning (FL) enables secure data analysis on interconnected devices. This study surveys authentication methods for FL, addressing security threats like model poisoning and enhancing trust in AI systems.

Keywords:
IoTauthenticationbehaviorbiometricblockchaincryptographyfederated learning

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Area of Science:

  • Internet of Things (IoT) security
  • Machine Learning privacy
  • Distributed Artificial Intelligence

Background:

  • IoT generates vast data, analyzed by machine learning, but centralized training raises privacy issues.
  • Federated Learning (FL) trains models locally, sharing only updates, mitigating privacy concerns.
  • FL systems face security threats like model poisoning and Byzantine attacks, necessitating robust authentication.

Purpose of the Study:

  • To provide a comprehensive survey of authentication mechanisms in Federated Learning (FL).
  • To examine FL authentication processes, challenges, and architectural considerations.
  • To classify existing FL authentication schemes by technology and system context.

Main Methods:

  • Review and evaluation of existing FL authentication schemes.
  • Classification of schemes based on enabling technologies (blockchain, cryptography, AI) and system contexts.
  • Analysis of datasets, experimental environments, and identification of research gaps.

Main Results:

  • Existing authentication schemes vary in effectiveness, limitations, and practicality.
  • Classification provides a structured overview of current approaches.
  • Identified open research challenges and future directions for secure FL.

Conclusions:

  • Robust authentication is crucial for secure and trustworthy Federated Learning.
  • This survey offers a foundational reference for advancing FL security.
  • Further research is needed to address identified challenges and enhance FL robustness.