The approach taken by FedTracker for ownership verification and traceability can be applied in other distributed learning paradigms by adapting its framework to suit specific requirements and constraints of different scenarios. The bi-level protection scheme consisting of global watermarks for ownership verification and local fingerprints for traceability can be modified or extended based on the characteristics of alternative distributed learning frameworks. By customizing parameters like trigger sets or fingerprint generation methods according to unique data privacy concerns or collaborative training setups in various paradigms, similar mechanisms for verifying ownership and tracing malicious behavior among participants can be implemented effectively across diverse distributed learning environments.