Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation

arXiv:2404.03088v1 Announce Type: new
Abstract: Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching …

abstract application arxiv attention collaborative concerns cs.ai cs.lg cs.ni eess.sp federated learning networks privacy robust security security concerns small studies training training models type wireless