Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach

TL;DR

This paper proposes a blockchain-based federated learning framework that uses smart contracts to fairly and transparently reward contributors based on their federated contribution, enhancing trust and incentivization.

Contribution

It introduces a novel reward-driven model leveraging smart contracts to measure and incentivize participant contributions in federated learning.

Findings

Smart contracts enable transparent incentivization in federated learning.

A new scalar measure of federated contribution is proposed.

The model has potential to improve enterprise adoption of federated learning.

Abstract

Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smart contracts on a blockchain to define incentivization rules for the contributors, which is based on a novel scalar quantity - federated contribution. Such a smart contract based reward-driven model has the potential to revolutionize the federated learning adoption in enterprises. Our contribution is two-fold: first is to show how smart contract based blockchain can be a very natural communication channel for federated learning. Second, leveraging this infrastructure, we can show how an intuitive measure of each agents' contribution can be built and integrated with the life cycle of the training and reward process.