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Federated learning is one of the most appealing alternatives to the standardcentralized learning paradigm, allowing a heterogeneous set of devices to traina machine learning model without sharing their raw data. However, it requires acentral server to coordinate the learning process, thus introducing potentialscalability and security issues. In the literature, server-less federatedlearning approaches like gossip federated learning and blockchain-enabledfederated learning have been proposed to mitigate these issues. In this work,we propose a complete overview of these three techniques proposing a comparisonaccording to an integral set of performance indicators, including modelaccuracy, time complexity, communication overhead, convergence time, and energyconsumption. An extensive simulation campaign permits to draw a quantitativeanalysis considering both feedforward and convolutional neural network models.Results show that gossip federated learning and standard federated solution areable to reach a similar level of accuracy, and their energy consumption isinfluenced by the machine learning model adopted, the software library, and thehardware used. Differently, blockchain-enabled federated learning represents aviable solution for implementing decentralized learning with a higher level ofsecurity, at the cost of an extra energy usage and data sharing. Finally, weidentify open issues on the two decentralized federated learningimplementations and provide insights on potential extensions and possibleresearch directions in this new research field.

The Cost of Training Machine Learning Models over Distributed Data Sources