  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.
