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.

翻译：联邦学习是标准集中式学习范式最具吸引力的替代方案之一，它允许异构设备在不共享原始数据的情况下训练机器学习模型。然而，该方法需要中央服务器协调学习过程，从而可能引入可扩展性和安全性问题。现有文献提出了无服务器联邦学习方法（如八卦联邦学习和区块链联邦学习）以缓解这些问题。本研究全面概述了这三种技术，并根据一组综合性能指标（包括模型准确率、时间复杂度、通信开销、收敛时间和能耗）进行了比较。通过大规模仿真，我们基于前馈神经网络和卷积神经网络模型进行了定量分析。结果表明，八卦联邦学习与标准联邦解决方案能达到相似的准确率，且其能耗受机器学习模型类型、软件库和硬件配置的影响。相比之下，区块链联邦学习以额外能耗和数据共享为代价，成为实现更高安全性去中心化学习的可行方案。最后，我们指出两种去中心化联邦学习实现中的未解决问题，并针对这一新兴研究领域提出潜在扩展方向和研究路径。