论文标题
联合学习和差异隐私：软件工具分析，Sherpa.ai FL框架和保护数据隐私的方法论指南
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
论文作者
论文摘要
人工智能服务在边缘的高需求也保留了数据隐私，这推动了适合这些要求的新机器学习范式的研究。联合学习具有通过分布式学习方法保护数据隐私的雄心，这些方法将数据置于其数据孤岛中。同样，通过衡量联合学习要素之间的通信中的隐私损失，差异隐私可以改善数据隐私的保护。联合学习和与数据隐私保护的挑战的预期匹配导致了几种支持其功能的软件工具的释放，但是他们缺乏对这些技术的所需统一愿景，以及支持其使用的方法学工作流程。因此，我们介绍了sherpa.ai联合学习框架，该框架建立在联合学习和差异隐私的整体观点之上。它是从如何使机器学习范式适应联合学习的研究以及基于联合学习和差异隐私开发人工智能服务的方法论指南的定义。我们展示了如何通过分类和回归用例遵循Sherpa.ai联合学习框架的方法学指南。
The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in their data silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use. Hence, we present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy. It results from the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.
