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Exponential growth of novel radical applications and services in sixth-generation (6G) networks is expected to increase the complexity of managing existing network infrastructures. In this context, the zero touch network and service management (ZSM) paradigm, which leverages AI, SDN, and NFV techniques, is seen as a promising solution to automatically manage and orchestrate network resources. However, due to the closed-loop operation and automated end-to-end framework in a distributed 6G network, the ZSM architecture, along with its potential benefits, is exposed to various security threats. A recently proposed solution to address privacy concerns is federated learning (FL), whereby distributed training is performed, and the aggregated model parameters, instead of clients' raw data, are forwarded to the global server. However, most of the existing FL and semi-supervised learning (SSL) models for intrusion …