Federated AI for building AI Solutions across Multiple Agencies
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
- 2021Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compressioncited by 0
- 2023An effective and efficient green federated learning method for one-layer neural networkscited by 0
- 2023An effective and efficient green federated learning method for one-layer neural networks22 Dec 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2021Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression18 Sep 2021
- 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates10 May 2021