To further evaluate the effectiveness of FAROS in various

The gap

Evidence profile

Sourced from the stated research gap and future-work section and inline gaps of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 5 journals. Those papers have been cited 118 times in total.

Supporting evidence — 5 representative gaps

• Federated Learning Paradigms for Privacy-Preserving Multi Organizational Threat Intelligence Sharing (2026) · Iconic Research and Engineering Journals · doi

  The gap between the need for collaborative training and the constraints imposed by regulatory, institutional, contractual, competitive, and technical barriers. The lack of mature solutions for inter-organizational trust, semantic interoperability, and technical governance. The need for more robust and scalable federated learning paradigms that can balance privacy, analytical utility, and operational resilience.

  generalstated research gap
  Keywords: gap between need collaborative training constraints imposed regulatory
• Fed-DT-EdgeGNN: Privacy-Preserving Federated Digital Twin Edge Intelligence for Secure VANET Communication (2026) · International Journal of Computer Science and Mobile Computing · doi

  The lack of proactive traffic management and attack foresight predictive intelligence in current VANET solutions. The lack of scalable, secure, and predictive solutions for future generation vehicular communication systems. The need for a framework that combines digital twin-based simulation, spatio-temporal graph neural networks, and federated learning with differential privacy.

  generalstated research gap
  Keywords: lack proactive traffic management attack foresight predictive intelligence
• Faros: robust federated learning with adaptive scaling against backdoor attacks (2026) · The Journal of Supercomputing · doi

  To further evaluate the effectiveness of FAROS in various federated learning scenarios. To explore the application of FAROS to other machine learning paradigms, such as distributed learning and transfer learning. To investigate the use of other defense techniques, such as anomaly detection and intrusion detection, to improve the robustness of federated learning systems.

  generalfuture-work section
  Keywords: further evaluate effectiveness faros various federated learning scenarios
• FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT (2024) · IEEE Access · cited 118× · doi

  Li, ‘‘Federated learning for vehicular internet of things: Recent advances and open issues,’’ IEEE Open Journal of the Computer Society, vol. Gaur, ‘‘Cyber security and privacy of connected and automated vehicles (cavs)-based federated learn- ing: challenges, opportunities, and open issues,’’ Federated Learning for IoT Applications, pp. Gaur, ‘‘Cyber security and privacy of connected and automated vehicles (cavs)-based kks federated learning: Challenges, opportunities, and open issues.

  generalinline gapsevidence 5/5
  Keywords: federated open learning issues gaur cyber security privacy connected automated vehicles cavs based challenges opportunities
• Unfederated: Open Challenges, Deployment Gaps, and Emerging Directions in Federated Learning (2026) · Archives of Computational Methods in Engineering · doi

  Niknam S, Dhillon HS, Reed JH (2020) Federated learning for wireless communications: motivation, opportunities, and 1 3Unfederated: Open Challenges, Deployment Gaps, and Emerging Directions in Federated Learningchallenges. 33 90/s2 3177358 1 3Unfederated: Open Challenges, Deployment Gaps, and Emerging Directions in Federated Learning82. Li X, Peng L, Wang Y-P, Zhang W (2025) Open challenges and opportunities in federated foundation models towards biomedical healthcare.

  generalinline gapsevidence 5/5
  Keywords: federated challenges learning open unfederated deployment gaps emerging directions opportunities niknam dhillon reed wireless communications

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