Verma, DC;
Julier, S;
Cirincione, G;
(2018)
Federated AI for building AI Solutions across Multiple Agencies.
In:
arXiv.
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Abstract
The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.
| Type: | Proceedings paper |
|---|---|
| Title: | Federated AI for building AI Solutions across Multiple Agencies |
| Event: | AAAI FSS-18: Artificial Intelligence in Government and Public Sector |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://arxiv.org/abs/1809.10036 |
| Language: | English |
| Additional information: | For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119536 |
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