# Federated AI for building AI Solutions across Multiple Agencies

arXiv: https://arxiv.org/abs/1809.10036 · HTML: https://mlsanity.com/paper/1809.10036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates](https://mlsanity.com/paper/2105.04153/) (2021)
- [Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression](https://mlsanity.com/paper/2109.08819/) (2021)
- [An effective and efficient green federated learning method for one-layer neural networks](https://mlsanity.com/paper/2312.14528/) (2023)

## 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.