Barriers to the Practical Adoption of Federated Machine Learning in Cross-company Collaborations

Tobias Müller, Nadine Gärtner, Nemrude Verzano, Florian Matthes

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Research in federated machine learning and privacy-enhancing technologies has spiked recently. These technologies could enable cross-company collaboration, which yields the potential of overcoming the persistent bottleneck of insufficient training data. Despite vast research efforts and potentially large benefits, these technologies are only applied rarely in practice and for specific use cases within a single company. Among other things, this little and specific utilization can be attributed to a small amount of libraries for a rich variety of privacy-enhancing methods, cumbersome design of end-to-end privacy-enhancing pipelines and unwieldy customizability to needed requirements. Hence, we identify the need for an easy-to-use privacy-enhancing tool to support and enable cross-company machine learning, suitable for varying scenarios and easily adjustable to the desired corresponding privacy-utility desiderata. This position paper presents the starting point for our future work aiming at the development of the described application.

Original languageEnglish
Pages (from-to)581-588
Number of pages8
JournalInternational Conference on Agents and Artificial Intelligence
StatePublished - 2022
Event14th International Conference on Agents and Artificial Intelligence , ICAART 2022 - Virtual, Online
Duration: 3 Feb 20225 Feb 2022


  • Anonymization
  • Big Data
  • Data Markets
  • Encryption
  • Federated Learning
  • Privacy-enhancing Techniques


Dive into the research topics of 'Barriers to the Practical Adoption of Federated Machine Learning in Cross-company Collaborations'. Together they form a unique fingerprint.

Cite this