Artificial intelligence across company borders
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Abstract (arXiv)
Artificial intelligence (AI) has become a valued technology in many companies. At the same time, a substantial potential for utilizing AI 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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jun 2021.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
- 2023Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learningcited by 2
- 2023Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosiscited by 2
- 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
- 2025Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trendscited by 1
- 2025Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends4 Oct 2025
- 2024PackVFL: Efficient HE Packing for Vertical Federated Learning1 May 2024
- 2023Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis26 Apr 2023
- 2023A Survey on Vertical Federated Learning: From a Layered Perspective4 Apr 2023
- 2023Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning28 Mar 2023