The proliferation of Enterprise Networks, characterized by heterogeneous devices, distributed data sources, and increasingly sophisticated cyber threats, has exposed the limitations of traditional perimeter-based security models. Guided by the principles of Zero Trust Architecture (ZTA), this paper presents a Zero-Trust (ZT)-Driven Federated Learning Algorithm for Privacy Enhancement (ZT-FL-PE), designed to safeguard model and data confidentiality in decentralized learning environments. By integrating ZTA’s “never trust, always verify” posture with Federated Learning’s (FL) decentralized training paradigm, the proposed framework eliminates the need for centralized data aggregation and significantly reduces the attack surface. The algorithm specifically targets two prominent threats to model privacy: property inference attacks (PIAs) and membership inference attacks (MIAs). We introduce adaptive verification mechanisms and privacy-preserving update transformations that enforce continuous authentication, constrain adversarial behavior, and strengthen resilience against inference-based exploitation. Experimental results demonstrate that ZT-FL-PE substantially enhances privacy protection while maintaining high model accuracy and imposing only low-to-moderate computational overhead, making it a practical and robust solution for modern ZT Enterprise environments.
Pule et al. (Thu,) studied this question.