Nowadays, machine learning algorithms continue to grow in complexity and require a substantial amount of computational resources and energy. For these reasons, there is a growing awareness of the development of new green algorithms and distributed AI can contribute to this. Federated learning (FL) is one of the most active research lines in machine learning, as it allows the training of collaborative models in a distributed way, an interesting option in many real-world environments, such as the Internet of Things, allowing the use of these models in edge computing devices. In this work, we present a FL method, based on a neural network without hidden layers, capable of generating a global collaborative model in a single training round, unlike traditional FL methods that require multiple rounds for convergence. This allows obtaining an effective and efficient model that simplifies the management of the training process. Moreover, this method preserve data privacy by design, a crucial aspect in current data protection regulations. We conducted experiments with large datasets and a large number of federated clients. Despite being based on a network model without hidden layers, it maintains in all cases competitive accuracy results compared to more complex state-of-the-art machine learning models. Furthermore, we show that the method performs equally well in both identically and non-identically distributed scenarios. Finally, it is an environmentally friendly algorithm as it allows significant energy savings during the training process compared to its centralized counterpart.

翻译：如今，机器学习算法复杂度持续攀升，需要消耗大量计算资源与能源。基于此，学界日益关注新型绿色算法的研发，而分布式人工智能可为此做出贡献。联邦学习作为机器学习领域最活跃的研究方向之一，能够以分布式方式训练协作模型，在物联网等众多现实场景中展现出重要价值，尤其适用于边缘计算设备。本文提出一种基于无隐藏层神经网络的联邦学习方法，与传统需要多轮训练才能收敛的联邦学习方法不同，该方法仅需单轮训练即可生成全局协作模型。这使得我们能够获得兼具高效性与有效性的模型，从而简化训练流程管理。该方法在设计上天然保护数据隐私，契合当前数据保护法规的关键要求。我们使用大规模数据集与大量联邦客户端开展实验。尽管基于无隐藏层网络模型，该方法在所有测试场景中均能保持与现有复杂机器学习模型相当的精度的竞争性结果。实验表明，该方法在同分布与非同分布场景中均表现优异。最后，该算法具有环境友好特性，与集中式训练相比，能在训练过程中显著节约能源消耗。