# Representation of Federated Learning via Worst-Case Robust Optimization Theory

arXiv: https://arxiv.org/abs/1912.05571 · HTML: https://mlsanity.com/paper/1912.05571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Generalized ADMM in Distributed Learning via Variational Inequality](https://mlsanity.com/paper/2104.12608/) (2021)

## Abstract (arXiv)

Federated learning (FL) is a distributed learning approach where a set of end-user devices participate in the learning process by acting on their isolated local data sets. Here, we process local data sets of users where worst-case optimization theory is used to reformulate the FL problem where the impact of local data sets in training phase is considered as an uncertain function bounded in a closed uncertainty region. This representation allows us to compare the performance of FL with its centralized counterpart, and to replace the uncertain function with a concept of protection functions leading to more tractable formulation. The latter supports applying a regularization factor in each user cost function in FL to reach a better performance. We evaluated our model using the MNIST data set versus the protection function parameters, e.g., regularization factors.