Representation of Federated Learning via Worst-Case Robust Optimization Theory

Saeedeh Parsaeefard; Iman Tabrizian; Alberto Leon Garcia

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arXiv · 1912.05571

Representation of Federated Learning via Worst-Case Robust Optimization Theory

Saeedeh Parsaeefard·Iman Tabrizian·Alberto Leon Garcia

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Abstract

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.

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Saeedeh Parsaeefard, Iman Tabrizian, Alberto Leon Garcia. 2019-12-11. Representation of Federated Learning via Worst-Case Robust Optimization Theory. https://arxiv.org/abs/1912.05571

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