DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 18 May 2021.
Builds on
17 of 49 references.
References · 17
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5 more
- 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
- 2020Wireless Federated Learning with Local Differential Privacycited by 50
- 2020FLaaS: Federated Learning as a Servicecited by 22
- 2019Decentralized & Collaborative AI on Blockchaincited by 21
- 2018Blockchain as a Service: A Decentralized and Secure Computing Paradigmcited by 2
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.