# Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

arXiv: https://arxiv.org/abs/2309.05525 · HTML: https://mlsanity.com/paper/2309.05525/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Survey on AI for 6G: Challenges and Opportunities](https://mlsanity.com/paper/2604.02370/) (2026)
- [FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View](https://mlsanity.com/paper/2507.20871/) (2025)
- [Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models](https://mlsanity.com/paper/2508.13625/) (2025)
- [Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks](https://mlsanity.com/paper/2512.18432/) (2025)
- [FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning](https://mlsanity.com/paper/2608.23031/) (2026)
- [ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics](https://mlsanity.com/paper/2609.07838/) (2026)

## Abstract (arXiv)

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.