This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts.

翻译：本文提出了一种基于代表性数据的分布式学习方法，将多个原始数据点转化为虚拟表示。与传统分布式学习方法（如联邦学习）缺乏人类可解释性不同，我们的方法使复杂的机器学习过程变得易于访问和理解。该方法通过将大规模数据集压缩为简洁格式，从而促进直观的人机交互。此外，该方法保持了隐私性和通信效率，并在使用原始数据训练的模型性能上与之匹配。仿真结果表明，我们的方法在准确性和收敛性方面与传统联邦学习相比具有竞争力或更优表现，尤其在复杂模型和客户端数量较多的场景下。该框架标志着将人类直觉与机器智能融合迈出了一步，有望增强人机学习界面与协作效果。