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Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization.

Saptarshi DE Chaudhury1, Likhith Reddy Morreddigari1, Matta Varun1

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This summary is machine-generated.

This study introduces a novel method for Federated Learning (FL) using blockchain and Local Differential Privacy (LDP). It incentivizes data sharing, ensuring only contributing nodes access trained models, enhancing security and participation.

Keywords:
Encrypted model parameterHyperLedger fabricfederated learninglocal differential privacysession key

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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.