FedFace is a privacy-preserving federated learning framework for multi-task face recognition and analysis. The system leverages self-supervised learning techniques and federated architecture to protect user privacy while providing accurate facial analysis across multiple tasks including identity recognition, gender classification, age estimation, and expression recognition. By distributing the training process across multiple devices without sharing raw facial data, FedFace addresses critical privacy concerns in biometric applications. Our approach incorporates differential privacy, secure aggregation, and self-supervised pre-training to enhance both privacy protection and model performance. Experimental results demonstrate that FedFace achieves competitive accuracy while providing strong privacy guarantees, making it suitable for real-world deployment scenarios where data privacy is paramount. The multi-task architecture efficiently performs multiple face analysis tasks using a single unified model, reducing computational overhead and improving overall system efficiency.