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.

该论文介绍了一种基于代表性的分布式学习方法，将多个原始数据点转化为虚拟表示。相比于传统的分布式学习方法如联邦学习，该方法能使复杂的机器学习过程可解释且易懂，通过将庞大的数据集压缩成可理解的格式来促进人机直观交互。此外，该方法能保持隐私和通信效率，并且在训练性能上与使用原始数据的模型相媲美。模拟结果表明，在复杂模型和更多客户端的场景下，该方法在准确性和收敛性方面与传统的联邦学习相比具有竞争力甚至更好。该框架在整合人类直觉和机器智能方面迈出了一步，有潜力增强人机学习界面和协作努力。