# Privacy-Preserving in Blockchain-based Federated Learning Systems arXiv: https://arxiv.org/abs/2401.03552 ยท HTML: https://mlsanity.com/paper/2401.03552/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 174 references that are in the map. - First posted on arXiv 7 Jan 2024. ## Builds on (most cited first, 30 of 31) - [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019) - [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019) - [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019) - [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019) - [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018) - [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018) - [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018) - [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021) - [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018) - [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022) - [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019) - [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018) - [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019) - [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020) - [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021) - [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020) - [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021) - [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018) - [Local Differential Privacy for Deep Learning](https://mlsanity.com/paper/1908.02997/) (2019) - [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020) - [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019) - [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021) - [Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks](https://mlsanity.com/paper/2204.09829/) (2022) - [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022) - [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023) - [Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning](https://mlsanity.com/paper/2011.12623/) (2020) - [Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems](https://mlsanity.com/paper/2304.13360/) (2023) - [HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection](https://mlsanity.com/paper/2204.04254/) (2022) - [An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation](https://mlsanity.com/paper/2208.07194/) (2022) - [Survey of Crosschain Communications Protocols](https://mlsanity.com/paper/2004.09494/) (2020) ## Built on by (most cited first, 7 of 7) - [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024) - [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024) - [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024) - [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025) - [GShield: Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2512.19286/) (2025) - [Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey](https://mlsanity.com/paper/2505.07882/) (2025) - [Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems](https://mlsanity.com/paper/2602.08290/) (2026) ## Abstract (arXiv) Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.