一种用于癌症研究的隐私保护、分布式与协作式基于FCM的学习方法
A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research
AI总结：
本文提出一种隐私保护的分布式协作式FCM学习方法，通过联邦学习提升癌症检测模型性能，在保障数据隐私合规的同时取得与文献相当的成果。
摘要：
分布式人工智能日益受到关注。本文作者提出了一种创新方法，以隐私保护的方式对基于粒子群优化的模糊认知图进行分布式学习。作者设计了一种协作式FCM训练方案，提供符合现行法规的数据隐私。该方法应用于癌症检测问题，证明模型性能通过联邦学习过程得到提升，并获得了与文献中相似的结果。
英文摘要：
Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
