Representation of Federated Learning via Worst-Case Robust Optimization Theory
This work addresses the challenge of handling data uncertainty in federated learning for distributed systems, but it appears incremental as it builds on existing optimization frameworks without introducing a fundamentally new approach.
The paper tackled the problem of federated learning by reformulating it using worst-case robust optimization theory to account for uncertainty in local data sets, resulting in a more tractable formulation that incorporates regularization factors to improve performance, as evaluated on the MNIST dataset.
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.