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.

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