DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities

TL;DR

This paper introduces DID-eFed, a federated learning as a service platform leveraging decentralized identities and smart contracts to enhance privacy, access management, and usability among hospitals and research institutions.

Contribution

It presents a novel FLaaS system integrating decentralized identities and smart contracts to improve privacy and access control in federated learning applications.

Findings

DID-eFed enables flexible access management for federated learning.

The system facilitates secure collaboration among hospitals and research institutions.

Decentralized identities improve trust and credibility in data sharing.

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.