Trustworthy Reputation for Federated Learning Leveraging Blockchain: A Demonstration

The inherent virtualization characteristics of 5G, 6G, and subsequent generations (xG) facilitate the modularity of components and their interfaces, leading to an increase in the number of stakeholders and necessitating more complex administrative relationships. In this demonstration, we present an open-source Decentralized Application (DApp) integrated with smart contracts deployed on a live Polygon testnet to support trustworthy Federated Learning (FL). FL enables clients to conduct local training and updates, while a central aggregation server processes these inputs for global FL training. We introduce a blockchain-based reputation system within such FL model to facilitate collaborative training of Machine Learning (ML) models. The deployed smart contracts on the Layer 2 Polygon blockchain calculate and store reputation scores on-chain for each FL client based on their contribution. This demonstration presents a visualization of the blockchain network parameters during the execution of blockchain-enabled smart contracts. The DApp offers an interactive interface for client registration, performance parameter submission, and reputation score calculation. We use Alchemy's dashboard for real-time monitoring of blockchain transactions and smart contract interactions. The open-source implementation is publicly available<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/farhanajaved/bc-fl-demo-l2 and the video of the demo is available at<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>https://youtu.be/B9J1gxjoKdM.