Fully Homomorphic Encryption (FHE) is a cryptographic technique that enables computations to be performed directly on encrypted data, without needing to decrypt it first. This capability has the potential to revolutionize how sensitive information is processed in a variety of fields, including Machine Learning (ML). In recent years, ML has been applied to numerous real-world problems, many of which rely on a client-server framework where sensitive data is sent to powerful servers for processing. Nevertheless, this structure poses significant privacy challenges, as client data must be shared with third parties for model training and inference, exposing it to potential breaches and misuse.