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Updated: Feb 11, 2026

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A scalable and secure federated learning authentication scheme for IoT.

Premkumar Chithaluru1,2, B Veera Jyothi3, Fahd S Alharithi4

  • 1Symbiosis Institute of Technology, Hyderabad Campus, Symbiosis International (Deemed University), Pune, India.

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|February 9, 2026
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Summary
This summary is machine-generated.

This study introduces ScLBS, a novel authentication scheme for Internet of Things (IoT) networks. ScLBS uses federated learning (FL) to enhance security and scalability while reducing resource consumption in IoT devices.

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Area of Science:

  • Computer Science
  • Network Security
  • Cryptography

Background:

  • Internet of Things (IoT) networks face significant authentication challenges due to resource constraints and dynamic topologies.
  • Traditional authentication methods like password-based and certificate-driven schemes are unsuitable for large-scale IoT deployments due to high overhead.
  • Lack of centralized trust infrastructures complicates secure communication in distributed IoT environments.

Purpose of the Study:

  • To propose ScLBS, a federated learning (FL)-based self-certified authentication scheme for distributed and sustainable IoT environments.
  • To enable decentralized public key derivation and enhance trust adaptation without relying on third-party certificate authorities.
  • To improve resistance against various attacks like impersonation, Sybil, and replay attacks in IoT networks.

Main Methods:

  • Integration of self-certified public key cryptography with FL-driven trust adaptation.
  • Implementation of a zero-knowledge mechanism and location-aware authentication.
  • Utilizing hierarchical key management with a [Formula: see text]-tree for efficient group rekeying and secrecy preservation.
  • Formal security verification using ProVerif under the Dolev-Yao adversary model.
  • Performance evaluation through NS-3 simulations and ablation analysis.

Main Results:

  • ScLBS demonstrates lower authentication delay and reduced message overhead compared to existing IoT authentication schemes.
  • Improved network utilization and decreased energy consumption were observed in simulations.
  • The scheme maintains bounded federated learning overhead while ensuring security.
  • Formal verification confirmed the secrecy of private and session keys and the correctness of authentication.

Conclusions:

  • ScLBS offers a balanced trade-off between security, scalability, and resource efficiency for constrained IoT networks.
  • The proposed scheme effectively addresses the fundamental challenges of secure and scalable authentication in IoT.
  • Federated learning integration proves beneficial for trust adaptation and decentralized key management in IoT environments.