# Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning arXiv: https://arxiv.org/abs/2403.19178 ยท HTML: https://mlsanity.com/paper/2403.19178/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 58 of its 145 references that are in the map. - First posted on arXiv 28 Mar 2024. ## Builds on (most cited first, 30 of 58) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018) - [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019) - [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021) - [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020) - [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017) - [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021) - [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019) - [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019) - [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018) - [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023) - [Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2008.12161/) (2020) - [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) - [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021) - [Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization](https://mlsanity.com/paper/2310.15080/) (2023) - [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) - [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021) - [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020) - [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019) - [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021) - [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019) - [FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update](https://mlsanity.com/paper/2312.05770/) (2023) - [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020) - [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020) - [Efficient Device Scheduling with Multi-Job Federated Learning](https://mlsanity.com/paper/2112.05928/) (2021) - [FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server](https://mlsanity.com/paper/2204.11536/) (2022) ## Built on by (most cited first, 9 of 9) - [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024) - [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025) - [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024) - [Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data](https://mlsanity.com/paper/2408.05678/) (2024) - [Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning](https://mlsanity.com/paper/2406.04076/) (2024) - [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024) - [Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout](https://mlsanity.com/paper/2507.10430/) (2025) - [Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications](https://mlsanity.com/paper/2506.09090/) (2025) - [Verifiable Split Learning via zk-SNARKs](https://mlsanity.com/paper/2511.01356/) (2025) ## Abstract (arXiv) While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL.