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Found 4 papers, 1 papers with code
Interaction and Conflict Management in AI-assisted Operational Control Loops in 6G
We propose "interaction and conflict management" (ICM) modules to achieve coherent, consistent and interactions among these ACLs.
Generalized ADMM in Distributed Learning via Variational Inequality
Due to the explosion in size and complexity of modern data sets and privacy concerns of data holders, it is increasingly important to be able to solve machine learning problems in distributed manners.
Robust Federated Learning by Mixture of Experts
We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models.
Representation of Federated Learning via Worst-Case Robust Optimization Theory
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.
