Artificial intelligence across company borders

TL;DR

This paper discusses how federated learning combined with domain adaptation can enable effective AI collaboration across companies without sharing sensitive data, addressing privacy and security concerns.

Contribution

It highlights the potential and implications of using federated learning with domain adaptation for cross-company AI applications.

Findings

Federated learning enables collaborative AI without data sharing.

Domain adaptation helps align models across different company data distributions.

This approach addresses privacy, intellectual property, and cybersecurity concerns.

Abstract

Artificial intelligence (AI) has become a valued technology in many companies. At the same time, a substantial potential for utilizing AI \emph{across} company borders has remained largely untapped. An inhibiting factor concerns disclosure of data to external parties, which raises legitimate concerns about intellectual property rights, privacy issues, and cybersecurity risks. Combining federated learning with domain adaptation can provide a solution to this problem by enabling effective cross-company AI without data disclosure. In this Viewpoint, we discuss the use, value, and implications of this approach in a cross-company setting.