Artificial Intelligence and Machine Learning are fundamental components for extracting meaningful insights from data. The growing reliance on data analytics and data-driven applications has raised concerns regarding privacy and security. Federated Learning, a key Privacy Enhancing Technology, offers a decentralized approach to model training, reducing the risks associated with centralized data collection and computation. However, Federated Learning is not entirely immune to privacy and security threats, such as inference attacks, data poisoning, and model inversion, which can compromise the confidentiality and integrity of data. This paper proposes a privacy threat model for Federated Learning, drawing inspiration from the LINDDUN framework while adapting it to address the unique challenges of Federated Learning environments. This proposed model incorporates the security concerns typically overlooked by LINDDUN. By employing the principles of privacy threat modeling, this approach identifies, categorizes, and mitigates privacy threats specific to Federated Learning, focusing on risks that extend beyond traditional LINDDUN threats.