# Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

arXiv: https://arxiv.org/abs/1906.10893 · HTML: https://mlsanity.com/paper/1906.10893/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis](https://mlsanity.com/paper/2503.10412/) (2025)
- [Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent](https://mlsanity.com/paper/2104.08100/) (2021)
- [Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement](https://mlsanity.com/paper/2201.02410/) (2022)
- [Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning](https://mlsanity.com/paper/2210.02873/) (2022)
- [Latency Analysis of Consortium Blockchained Federated Learning](https://mlsanity.com/paper/2105.04087/) (2021)
- [Federated Learning Method for Preserving Privacy in Face Recognition System](https://mlsanity.com/paper/2403.05344/) (2024)
- [BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse](https://mlsanity.com/paper/2410.21675/) (2024)

## Abstract (arXiv)

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.