联邦学习和差分隐私：软件工具分析，Sherpa.ai FL框架和保护数据隐私的方法指南
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
摘要：
边缘人工智能服务的高需求也保护了数据隐私，推动了对符合这些需求的新型机器学习范式的研究。联邦学习的目标是通过分布式学习方法来保护数据隐私，这些方法将数据保存在数据孤岛中。同样，差分隐私通过测量联邦学习的元素之间的通信中的隐私损失来实现对数据隐私的保护。联邦学习和差分隐私与数据隐私保护挑战的前瞻性匹配导致了几个支持其功能的软件工具的发布，但它们缺乏对这些技术所需的统一愿景，以及支持其使用的方法工作流程。因此，我们提出了Sherpa.ai联邦学习框架，该框架建立在联邦学习和差分隐私的整体视图上。它源于对如何使机器学习范式适应联邦学习的研究，以及基于联邦学习和差分隐私开发人工智能服务的方法指南的定义。我们展示了如何通过分类和回归用例来遵循Sherpa.ai联邦学习框架的方法指南。
英文摘要：
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.
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。
