【Author】
Geng, Jiahui; Kanwal, Neel; Jaatun, Martin Gilje; Rong, Chunming
【Source】PROCEEDINGS OF EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING (EASE 2021)
【Abstract】We have entered the era of big data, and it is considered to be the fuel for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals' privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.
【Keywords】decentralized identity; blockchain; federated learning; FLaaS
【标题】DID-eFed：促进具有去中心化身份的联邦学习即服务
【摘要】我们已经进入大数据时代，被认为是人工智能应用蓬勃发展的燃料。欧盟通用数据保护条例 (GDPR) 的颁布引发了人们对大数据中个人隐私的担忧。联邦学习 (FL) 作为一种功能性解决方案出现，可以帮助构建在多方之间共享的高性能模型，同时仍符合用户隐私和数据机密性要求。尽管 FL 已在实际应用中得到深入研究和使用，但对其作为 FLaaS（联邦学习即服务）的前景和应用对感兴趣的第三方的研究仍然有限。在本文中，我们提出了一个 FLaaS 系统：DID-eFed，其中 FL 由去中心化身份 (DID) 和智能合约促进。 DID 在我们的系统中实现了更灵活和可信的去中心化访问管理，而智能合约提供了一个无摩擦且不易出错的过程。我们特别描述了我们的 DID-eFed 在医院和研究机构之间启用 FLaaS 的场景。
【关键词】去中心化身份；区块链；联邦学习； FLaaS
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