Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approaches are confronted with obstacles such as system heterogeneity, statistical heterogeneity, and incentive design. How to deal with these challenges and further improve the efficiency and performance of multi-party learning has become an urgent problem to be solved. In this paper, we propose a novel contrastive multi-party learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

翻译：多党学习为在法律和实际限制下以分散的数据培训联合模式提供了解决办法,但是,传统的多党学习方法面临着系统差异、统计差异和激励设计等障碍;如何应对这些挑战和进一步提高多党学习的效率和绩效已成为迫切需要解决的问题;在本文件中,我们提议建立一个新的、与众不同的多党学习框架,以完善知识和与负责的激励机制分享知识;由于现有的天真的模型参数平均法与神经网络的学习范式相矛盾,我们模拟人类认知和通信进程,并将多党学习类推为多对一知识分享问题;这种方法能够以透明的方式整合每个客户获得的明确知识,而不披露隐私,并减少对数据分发和通信环境的依赖;拟议办法在各种情景下显著改进模型业绩,我们通过对几个真实世界数据集的实验就证明了这一点。