Automated Collaborator Selection for Federated Learning with Multi-armed Bandit Agents

Rapid change in sensitive behaviour and profile of distributed mobile network elements necessitates privacy preserving distributed learning mechanism such as Federated Learning. Moreover, this mechanism needs to be robust that seamlessly sustains the jointly trained model accuracy. In order to provide a automated management of the learning process in FL on datasets that are not independently and identically distributed (non-iid), we propose a Multi-Arm Bandit (MAB) based method that helps the federation to select the nodes that bene.ts the overall model. This automated selection of the training nodes throughout each round yielded an improvement in accuracy, while decreasing network footprint.

Automated summary

A method based on Multi-Arm Bandit is proposed for privacy preserving distributed learning mechanism like Federated Learning, aiming to automatically select nodes that benefit the overall model accuracy in datasets that are not independently and identically distributed (non-iid), resulting in improved accuracy and decreased network footprint.