FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification

arXiv:2404.08028v1 Announce Type: new
Abstract: Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge traffic classification. Despite its recognized advantages, FL encounters obstacles linked to statistical data heterogeneity and labeled data scarcity during the training of single-task models for machine learning-based traffic classification, leading to hindered learning performance. In response to these challenges, adopting a hard-parameter sharing multi-task …

abstract advantages application arxiv challenges classification cs.dc cs.lg data data privacy diverse edge example federated learning multi-task learning network network edge obstacles privacy solution statistical traffic type