BC-FL k-means: A Blockchain-based Framework for Federated Clustering

This work presents a novel framework to train clustering models collaboratively without compromising accuracy while accommodating privacy and security in a decentralized manner. Our decentralized collaborative learning model removes the single point of failure and excludes unreliable input by designing a committee-based consensus method in a blockchain-based federated learning, which is equipped with a reputation system. We present a prototype implementation of our approach and show that its performance is comparable with centralized clustering regardless of the distribution of data among devices.