DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
This work addresses the need for privacy-compliant FL services for third parties like healthcare organizations, but it appears incremental as it builds on existing FL concepts with new identity management.
The paper tackles the challenge of implementing Federated Learning as a Service (FLaaS) by proposing DID-eFed, a system that uses decentralized identities and smart contracts to facilitate flexible and credible access management, specifically applied to hospitals and research institutions.
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.