How Much Does It Cost to Train a Machine Learning Model over Distributed Data Sources?. (arXiv:2209.07124v1 [cs.LG])

Federated learning (FL) is one of the most appealing alternatives to the
standard centralized learning paradigm, allowing heterogeneous set of devices
to train a machine learning model without sharing their raw data. However, FL
requires a central server to coordinate the learning process, thus introducing
potential scalability and security issues. In the literature, server-less FL
approaches like gossip federated learning (GFL) and blockchain-enabled
federated learning (BFL) have been proposed to mitigate these issues. In this
work, we propose a complete …

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