Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.
翻译:各个行业之间,各组织及其客户、伙伴、供应商和内部团队之间为协作和创新而共享数据的比例不断增加,但许多企业由于不同区域的监管限制、在移动大量数据方面的绩效问题或保持自主的要求而受到限制,无法自由分享数据。在这种情况下,企业可以受益于联合学习的概念,即在不同地点建立机器学习模式。在本文件中,我们引入了一个通用框架,即BDSP, 以便在企业之间分享基于“障碍链”和“联合学习技术”的数据。具体地说,我们提议建立一个透明度贡献会计机制,用以估算数据估值,并落实一个供进一步评估的验证概念。广泛的实验结果表明,拟议的BDSP具有竞争性,培训精度更高,增加了5%以上,通信管理费较低,比基线方法减少了3倍。