Gimme That Model!: A Trusted ML Model Trading Protocol

Amorós, Laia; Hafiz, Syed Mahbub; Lee, Keewoo; Tol, M. Caner

doi:10.1007/978-3-030-77287-1_11

Cited by 2 publications

(1 citation statement)

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“…buying a pre-trained model for Transfer Learning (TL) or using Machine Learning as a Service (MLaaS). In [61], a DLT based marketplace is designed for the exchange of TL models, whereas [62] proposes a protocol leveraging HE to enable buyers of models to verify that they received the correct model. A blockchain based platform for IoT devices to get incentives for a contribution in an FL procedure is described in [63].…”

Section: A Trust For Aimentioning

confidence: 99%

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

Krummacker

Schotten

2023

IEEE Open J. Commun. Soc.

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While 5G networks are driving a growing number of use cases in the fields of Internet of Things (IoT) and industrial applications, the vision of the next generation of mobile communications systems already includes concepts massively transforming the way people will interact with the digital world through the network, as humans are shifting into the center of diverse network driven applications. Envisaged use cases and possibilities to provide services and resources in a distributed manner render an architectural solution for trust establishment a critical component of 6G networks. This survey provides an overview of terms and visions related to the topic of trust in general and in mobile communications systems. Requirements for an end-to-end trust building framework are derived, in order to give a starting point for the design process of a trust anchor service as a component of 6G networks.

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Section: A Trust For Aimentioning

confidence: 99%

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

Krummacker

Schotten

2023

IEEE Open J. Commun. Soc.

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From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

Herm

Janiesch

Reijers

et al. 2021

Lecture Notes in Computer Science

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Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA.

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Gimme That Model!: A Trusted ML Model Trading Protocol

Cited by 2 publications

References 4 publications

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

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