Federated learning for 6G security: a survey on threats, solutions and research directions

Chamitha De Alwis
,
Ons Aouedi
,
Jiaming Xu
,
Shen Wang
,
Yushan Siriwardhana
,
Tharaka Hewa

School of Computing Engineering and Creative Industries
,
University of Luxembourg
,
University College Dublin
,
University of Oulu
,
Centre Tecnològic Telecomunicacions Catalunya

Research Output:

Contribution to journal

Article

Peer-review

Open access

Abstract

The Sixth-Generation (6G) are already in the horizon, owing to advents of communication technologies towards enabling intelligent applications and services.} Federated Learning (FL) is a distributed Artificial Intelligence (AI) technology that underpins 6G communication technologies and applications. Interestingly, FL is also a promising contender to enhance 6G security. This paper presents a comprehensive and up-to-date review of FL-enabled 6G security. The paper explores security threats in FL for 6G, threats in FL for 6G, and threats shared across FL and 6G. Subsequently, how FL can be utilized to strengthen 6G security in the Radio Access Network (RAN), Open RAN (O-RAN), network edge, and network orchestration and core is presented. In addition, FL is for 6G application and service security across various emerging applications, ranging from Connected Autonomous Vehicles (CAVs) to the envisaged metaverse applications. The paper then consolidates lessons learned, projects, and proposes future research directions to establish the role of FL in strengthening 6G security.