Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization

Area of Science:

• Blockchain Technology
• Machine Learning
• Cybersecurity

Background:

• Federated Learning (FL) adoption is growing, but secure access to trained models for participants remains a significant challenge.
• Existing FL systems struggle to restrict model access to only active contributors, posing security and fairness issues.
• Local Differential Privacy (LDP) offers data obfuscation but needs integration with incentive mechanisms for effective FL.

Purpose of the Study:

• To propose a novel methodology for incentivizing model parameter sharing in Federated Learning (FL) under Local Differential Privacy (LDP).
• To ensure that only actively participating nodes can access updated global models, addressing a key challenge in current FL systems.
• To leverage blockchain technology for secure, decentralized management of FL processes and model access.

Main Methods:

• Developed a token-based incentive mechanism where nodes sharing less obfuscated data under LDP receive more tokens.
• Utilized HyperLedger Fabric (HLF), a permissioned blockchain, for local parameter sharing and global parameter updates.
• Implemented chaincodes (smart contracts) within HLF to manage the token distribution and model access control.

Main Results:

• Nodes sharing less perturbed data under LDP are rewarded with tokens, enabling access to encrypted model parameters.
• Nodes contributing less or sharing highly perturbed data earn fewer tokens, potentially restricting their access to updated global models.
• Experimental results demonstrate the feasibility and effectiveness of the proposed blockchain-based FL approach.

Conclusions:

• The proposed methodology successfully incentivizes model parameter sharing in LDP-enabled FL using blockchain.
• Access control to trained models is effectively managed, ensuring only contributing nodes can retrieve updated parameters.
• The blockchain-based approach enhances security, mitigates single points of failure, and validates the feasibility of the system.