# Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

arXiv: https://arxiv.org/abs/2403.00873 · HTML: https://mlsanity.com/paper/2403.00873/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 185 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 30 of 41)

- [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)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (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)
- [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)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [Communication-Efficient Adaptive Federated Learning](https://mlsanity.com/paper/2205.02719/) (2022)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [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)
- [DoCoFL: Downlink Compression for Cross-Device Federated Learning](https://mlsanity.com/paper/2302.00543/) (2023)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing](https://mlsanity.com/paper/2110.07136/) (2021)
- [Blockchain for Internet of Things: A Survey](https://mlsanity.com/paper/1906.00245/) (2019)
- [HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse](https://mlsanity.com/paper/2211.03300/) (2022)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022)
- [A Survey on the Security of Blockchain Systems](https://mlsanity.com/paper/1802.06993/) (2018)
- [A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction](https://mlsanity.com/paper/2305.07854/) (2023)
- [VeriSmart: A Highly Precise Safety Verifier for Ethereum Smart Contracts](https://mlsanity.com/paper/1908.11227/) (2019)
- [BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework](https://mlsanity.com/paper/2205.10568/) (2022)

## Built on by (most cited first, 5 of 5)

- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning](https://mlsanity.com/paper/2602.21841/) (2026)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)
- [The Power of Backdoor Absorption in Community Training](https://mlsanity.com/paper/2607.06643/) (2026)
- [Secure Decentralized Federated Learning via Gossip and Virtual Voting](https://mlsanity.com/paper/2607.08651/) (2026)

## Abstract (arXiv)

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.