  Federated learning is one of the most appealing alternatives to the standard
centralized learning paradigm, allowing a heterogeneous set of devices to train
a machine learning model without sharing their raw data. However, it requires a
central server to coordinate the learning process, thus introducing potential
scalability and security issues. In the literature, server-less federated
learning approaches like gossip federated learning and blockchain-enabled
federated learning have been proposed to mitigate these issues. In this work,
we propose a complete overview of these three techniques proposing a comparison
according to an integral set of performance indicators, including model
accuracy, time complexity, communication overhead, convergence time, and energy
consumption. An extensive simulation campaign permits to draw a quantitative
analysis considering both feedforward and convolutional neural network models.
Results show that gossip federated learning and standard federated solution are
able to reach a similar level of accuracy, and their energy consumption is
influenced by the machine learning model adopted, the software library, and the
hardware used. Differently, blockchain-enabled federated learning represents a
viable solution for implementing decentralized learning with a higher level of
security, at the cost of an extra energy usage and data sharing. Finally, we
identify open issues on the two decentralized federated learning
implementations and provide insights on potential extensions and possible
research directions in this new research field.
