Decentralized Federated Learning with Blockchain-Based Incentive Mechanisms

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, a key challenge in the widespread adoption of FL is the incentive problem: motivating participants to contribute their valuable data and computational resources. This paper proposes a novel decentralized federated learning system incorporating a blockchain-based incentive mechanism. The system leverages smart contracts on a blockchain to reward participants based on their contributions, enhancing the scalability, robustness, and trust of the FL process. The core claim is that incentivizing participation is a significant hurdle in FL, and this work presents a mechanism to address this. We detail the design of the system, focusing on the blockchain architecture, the smart contract implementation for reward distribution, and the overall protocol for federated learning. The system aims to create a self-regulating and trustworthy FL environment where participants are actively encouraged to contribute, ultimately leading to more robust and efficient model training.