# PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks

arXiv: https://arxiv.org/abs/1912.07860 · HTML: https://mlsanity.com/paper/1912.07860/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 18 of 18)

- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models](https://mlsanity.com/paper/2310.06278/) (2023)
- [Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments](https://mlsanity.com/paper/2110.11006/) (2021)
- [FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks](https://mlsanity.com/paper/2310.02113/) (2023)
- [Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach](https://mlsanity.com/paper/2104.13092/) (2021)
- [Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach](https://mlsanity.com/paper/2107.10243/) (2021)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning](https://mlsanity.com/paper/2502.16406/) (2025)
- [ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning](https://mlsanity.com/paper/2601.22302/) (2026)
- [A Comprehensive Study on Artificial Intelligence Algorithms to Implement Safety Using Communication Technologies](https://mlsanity.com/paper/2205.08404/) (2022)
- [Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations](https://mlsanity.com/paper/2311.10832/) (2023)
- [FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning](https://mlsanity.com/paper/2502.16396/) (2025)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

In the fifth-generation (5G) networks and the beyond, communication latency and network bandwidth will be no more bottleneck to mobile users. Thus, almost every mobile device can participate in the distributed learning. That is, the availability issue of distributed learning can be eliminated. However, the model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients amongst multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE. A case-study shows how the proposed PIRATE contributes to the distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine-resilient learning framework.