Project Description: Blockchain-Based Federated Learning with SMPC Model Verification Against Poisoning Attacks
Introduction
In the rapidly evolving landscape of machine learning and data privacy, federated learning (FL) has emerged as a promising paradigm. By allowing model training across decentralized devices while keeping data local, federated learning enhances privacy and reduces the risk of data breaches. However, this approach is not without its vulnerabilities, especially to model poisoning attacks, where malicious participants can inject harmful updates to compromise the integrity of the global model. This project proposes a robust framework that integrates blockchain technology with secure multi-party computation (SMPC) for verifying model updates, effectively safeguarding against these poisoning attacks.
Objectives
1. Develop a Federated Learning Framework: Create a decentralized federated learning system that allows multiple clients to collaboratively train a machine learning model without sharing their private data.
2. Integrate Blockchain Technology: Utilize blockchain features, such as transparency and immutability, to maintain a tamper-proof record of all model updates and client interactions.
3. Implement SMPC for Model Verification: Incorporate secure multi-party computation to validate the integrity of the model updates received from clients. This ensures that only benign updates are integrated into the global model.
4. Establish Robust Mechanisms Against Poisoning Attacks: Design and implement strategies to detect and mitigate potential poisoning attacks, enhancing the overall security and reliability of the federated learning system.
Methodology
1. Architecture Design:
– Develop a decentralized federated learning architecture incorporating clients, a central server, and a blockchain network.
– Clients will perform local model training and send updates to the server, which aggregates these updates into a global model.
2. Blockchain Integration:
– Deploy a private or permissioned blockchain to log all model updates, client identities, and timestamps.
– Use smart contracts to enforce rules governing model updates and validate the compliance and authenticity of clients.
3. Secure Multi-Party Computation:
– Implement SMPC protocols to enable clients to jointly compute model updates without revealing their local models or data.
– This step will facilitate collaborative verification of model updates before they are sent to the central server.
4. Poisoning Attack Detection Mechanisms:
– Develop algorithms to identify anomalous updates that deviate significantly from benign patterns. This might include statistical tests, anomaly detection techniques, or machine learning classifiers specifically trained to spot malicious behavior.
– Implement a heuristic or reputation-based system where clients are rewarded or penalized based on their update contributions.
Expected Outcomes
1. A well-defined and functional federated learning system that prioritizes data privacy and user participation.
2. A blockchain-based infrastructure that provides traceability, accountability, and trust among clients and the central server.
3. An efficient SMPC verification mechanism that ensures the integrity of model updates while maintaining privacy.
4. Enhanced resilience against poisoning attacks, with demonstrable metrics assessing the system’s performance and security.
Applications
The developed framework can be applicable across various domains that require secure and privacy-preserving machine learning, such as:
– Healthcare: Collaborative medical research without compromising patient confidentiality.
– Finance: Decentralized fraud detection systems that leverage data from multiple institutions without exposing sensitive financial information.
– Smart Cities: Secure data aggregation and predictive modeling for urban planning using IoT data.
Conclusion
This project aims to pioneer a secure federated learning paradigm by synergizing blockchain technology and secure multi-party computation. By safeguarding against poisoning attacks, this innovative framework can enhance trust in collaborative machine learning systems, ultimately propelling advancements in our increasingly data-driven society. The successful implementation and validation of this project could serve as a foundational model for future developments in the field of secure federated learning.
