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Related Experiment Videos

BC-AWFedAvg: blockchain-assisted adaptive federated learning for secure RAN Slicing in beyond-5G networks.

Mabrouka Zemzemi1, Jalel Eddine Hajlaoui2, Adel Sharar Aldalbahi3

  • 1Tunisia Polytechnic School, Carthage, Tunisia. zamzamkmk@gmail.com.

Scientific Reports
|June 29, 2026
PubMed
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This study introduces BC-AWFedAvg, a novel framework enhancing security and robustness for federated learning in beyond-5G Open Radio Access Networks (O-RAN). It ensures quality-of-service while protecting against adversarial attacks in network slicing.

Area of Science:

  • Telecommunications Engineering
  • Network Security
  • Artificial Intelligence

Background:

  • Beyond-5G networks face radio resource management challenges due to diverse services and virtual network operators.
  • Federated learning in Open Radio Access Networks (O-RAN) requires robust and secure methods for network slicing.

Purpose of the Study:

  • To present BC-AWFedAvg, a layered framework for federated deep reinforcement learning in O-RAN network slicing.
  • To enhance robustness, privacy, and quality-of-service under adversarial conditions.

Main Methods:

  • Developed a layered framework integrating adaptive aggregation, blockchain governance, secure aggregation, and differential privacy.
  • Separated learning, governance, and storage functions for coordinated, private training.

Related Experiment Videos

  • Conducted simulations to evaluate framework performance.

    • Main Results:

    • The BC-AWFedAvg framework demonstrated improved robustness against adversarial scenarios.
    • Maintained acceptable quality-of-service performance despite security enhancements.
    • The layered approach effectively preserved privacy by limiting exposure of individual updates.

    Conclusions:

    • Combining adaptive aggregation, blockchain, secure aggregation, and differential privacy is a promising direction for secure federated learning in next-generation wireless networks.
    • The proposed framework offers a viable solution for secure and efficient radio resource management in O-RAN network slicing.