# Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework

arXiv: https://arxiv.org/abs/2406.17172 · HTML: https://mlsanity.com/paper/2406.17172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Quantum Federated Learning Experiments in the Cloud with Data Encoding](https://mlsanity.com/paper/2405.00909/) (2024)
- [Learning to Harness Bandwidth with Multipath Congestion Control and Scheduling](https://mlsanity.com/paper/2105.14271/) (2021)
- [FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning](https://mlsanity.com/paper/2206.12899/) (2022)

## Built on by (most cited first, 1 of 1)

- [Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things](https://mlsanity.com/paper/2505.23792/) (2025)

## Abstract (arXiv)

This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks. Using blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients, enhancing the security of the global learning process. Moreover, secure and reliable trust computation is essential for remote work and collaboration. The robust ZTA framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion. We introduce an adaptive algorithm that dynamically adjusts to varying user contexts, using unsupervised clustering to detect novel anomalies, like zero-day attacks. To ensure a reliable and scalable trust computation, we develop an algorithm that dynamically adapts to varying user contexts by employing incremental anomaly detection and clustering techniques to identify and share local and global anomalies between nodes. Future directions include scalability improvements, Dirichlet process for advanced anomaly detection, privacy-preserving techniques, and the integration of post-quantum cryptographic methods to safeguard against emerging quantum threats.