# Blockchained On-Device Federated Learning

arXiv: https://arxiv.org/abs/1808.03949 · HTML: https://mlsanity.com/paper/1808.03949/

## Key facts

- Cited by 106 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2018.

## Built on by (most cited first, 30 of 106)

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- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [Mechanisms that Incentivize Data Sharing in Federated Learning](https://mlsanity.com/paper/2207.04557/) (2022)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [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)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [The Cost of Training Machine Learning Models over Distributed Data Sources](https://mlsanity.com/paper/2209.07124/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee](https://mlsanity.com/paper/2108.00365/) (2021)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [IronForge: An Open, Secure, Fair, Decentralized Federated Learning](https://mlsanity.com/paper/2301.04006/) (2023)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things](https://mlsanity.com/paper/2304.12889/) (2023)
- [OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning](https://mlsanity.com/paper/2107.05252/) (2021)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling](https://mlsanity.com/paper/2111.14008/) (2021)

## Abstract (arXiv)

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.