Project Background:
Blockchain technology combines two cutting-edge fields to address data privacy, security, and decentralization challenges. It provides a decentralized and immutable ledger for recording transactions, ensuring transparency and integrity in data sharing. However, traditional blockchain systems suffer from scalability and privacy limitations when handling sensitive data. Federated learning enables collaborative model training across distributed devices while keeping data local, thus preserving privacy. By integrating with blockchain technology, this project aims to overcome existing blockchain systems scalability and privacy challenges.
Federated learning allows participants to train machine learning models on their local data without exposing sensitive information to a central server. The blockchain ledger ensures the integrity and transparency of model updates, enabling secure and decentralized collaboration among participants. This novel approach holds promise for various applications where data sharing and collaborative model training are essential. By leveraging the synergies between blockchain technology and federated learning, this seeks to advance privacy-preserving and scalable solutions for data-driven applications in a decentralized manner.
Problem Statement
- Traditional blockchain systems may lack adequate privacy measures, exposing sensitive data to all participants in the network involving personal or proprietary information.
- Data remains siloed on individual devices, limiting the potential for collaborative model training and knowledge sharing across different participants in the network.
- The distributed nature of blockchain networks introduces security risks, including potential vulnerabilities in smart contracts, consensus algorithms, and data storage mechanisms.
- Determining data ownership and access rights within a blockchain-based framework can be challenging, leading to participant conflicts and disputes.
- Compliance with data protection regulations becomes more complex in blockchain-based federated learning environments due to the decentralized nature of data processing and storage.
- Integrating with blockchain technology may face interoperability challenges hindering seamless communication among different blockchain networks.
Aim and Objectives
- Integrate federated learning with blockchain technology to enhance privacy, security, and decentralization in collaborative machine learning.
- Develop a framework for secure and privacy-preserving federated learning on blockchain networks.
- Enhance scalability by leveraging blockchain distributed consensus mechanism for efficient model aggregation.
- Ensure data privacy by keeping sensitive information decentralized and encrypted on the blockchain.
-
Enable decentralized collaboration among participants while preserving data ownership and access rights.
- Ensure compliance with data protection regulations and standards in federated learning on blockchain.
Contributions to Blockchain Technology using Federated Learning
- Introducing decentralized and encrypted data storage on the blockchain to preserve privacy in collaborative machine learning.
- Leveraging blockchain tamper-proof ledger and cryptographic techniques to ensure the integrity and security of federated learning transactions.
- Enabling secure and decentralized collaboration among participants while maintaining data ownership and control.
- Utilizing distributed consensus mechanism for efficient and scalable model aggregation in federated learning.
- Facilitating compliance with data protection regulations and standards by integrating privacy-preserving techniques into federated learning on blockchain.
Datasets for Blockchain Technology using Federated Learning
- Federated Medical Records Dataset
-
Federated Financial Transaction Dataset
-
Federated IoT Data
-
Federated Image Dataset
-
Federated Text Dataset
-
Federated Healthcare Dataset
-
Federated Sensor Data
-
Federated Customer Data
-
Federated Social Media Data
-
Federated E-commerce Data
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
