Published January 1, 2026 | Version v1
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Federated Learning for 6G Security: A Survey on Threats, Solutions, and Research Directions

Authors/Creators

  • 1. Univ Bedfordshire, Sch Comp, Engn & Creat Ind, Luton, England
  • 2. Univ Luxembourg, SnT, SIGCOM, Esch Sur Alzette, Luxembourg
  • 3. Univ Coll Dublin, Sch Comp Sci, Network Softwarizat & Secur Labs NetsLab, Dublin, Ireland
  • 4. Univ Oulu, Ctr Wireless Commun, Oulu, Finland
  • 5. Ctr Tecnolg Telecomunicac Catalunya CTTC, Barcelona 08860, Spain

Description

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. © 1998-2012 IEEE.

Notes

Received 7 June 2025; revised 14 December 2025; accepted 28 January 2026. Date of publication 10 February 2026; date of current version 23 February 2026. This work was supported in part by EU Marie Sk\u0142odowska-Curie Staff Exchange project ENSURE6G under Grant 101182933, in part by the EU COST Action CA22104 (Behavioural Next Generation in Wireless Networks for Cyber Security), 6G SNS JU Robust-6G Project under Grant 101139068, and in part by Research Ireland under the CONNECT Phase 2 Project under Grant 13/RC/2077 P2. (Corresponding author: Madhusanka Liyanage.) Chamitha de Alwis is with the School of Computing, Engineering, and Creative Industries, University of Bedfordshire, U.K. (e-mail: chamitha@ieee.org).

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