Artificial intelligence systems are widely deployed across various fields, however, they still face severe ethical challenges that concern violations of user privacy, bias, a lack of accountability for decisions made, or unintended consequences and misuse. Such problems can erode user trust in Artificial Intelligence and stifle its wider adoption in critical areas like healthcare and finance. In light of these issues, this work presents a new federated learning-based framework that involves differential privacy in order to build a secure and fair Artificial Intelligent system. By employing decentralized data processing to protect the privacy of users, and utilizing multiple fine-tuning modalities to create fair sampling subsets, the framework minimizes bias from foundational models. Besides, the Decision Verification Layer verifies whether the outputs of each model comply with the pre-established ethical parameters, contributing to the responsibility and transparency of the resulting decisions. A Confidence Scoring Mechanism is also introduced to make the predictions more credible by estimating the predicted reliability of the model to the users. The incorporation of these ethical concerns makes our framework an effective guide to the ethical design and deployment of such systems.