  Federated learning (FL) is an emerging collaborative machine learning method
to train models on distributed datasets with privacy concerns. To properly
incentivize data owners to contribute their efforts, Shapley Value (SV) is
often adopted to fairly assess their contribution. However, the calculation of
SV is time-consuming and computationally costly. In this paper, we propose
FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a
feasible SV based profit distribution. In FedCoin, blockchain consensus
entities calculate SVs and a new block is created based on the proof of Shapley
(PoSap) protocol. It is in contrast to the popular BitCoin network where
consensus entities "mine" new blocks by solving meaningless puzzles. Based on
the computed SVs, a scheme for dividing the incentive payoffs among FL clients
with nonrepudiation and tamper-resistance properties is proposed. Experimental
results based on real-world data show that FedCoin can promote high-quality
data from FL clients through accurately computing SVs with an upper bound on
the computational resources required for reaching consensus. It opens
opportunities for non-data owners to play a role in FL.

Cryptography and Security;Machine Learning;Machine Learning