Data-privacy-preserving federated learning in cyber-physical systems
Data-privacy-preserving federated learning in cyber-physical systems
https://www.nature.com/collections/gahgfaadbj
Publish Date: 2026-05-27 07:38:00
Source Domain: www.nature.com
This Scientific Reports Collection welcomes original research that leverages real‑world applications to explore data‑privacy‑preserving federated learning frameworks tailored for cyber‑physical systems. Narrative review articles are also welcomed for consideration in our sister journal Scientific Reviews. For further information, please view the ‘Participating Journals’ below.
Cyber‑Physical Systems (CPS) are increasingly integrated into critical infrastructure, such as intelligent transportation and industrial automation. The vast amounts of distributed data generated are often sensitive, raising concerns about privacy and security. Federated Learning has recently emerged as a core methodology for collaborative model training without centralised data sharing.
