The more critical infrastructures, financial services and IoT systems are using digital ecosystems, the more people have wanted to have continuous authentication processes that are sensitive to privacy. Conventional methods like fixed passwords and one-time biometrics are becoming susceptible to a spoofing attack, credential theft, and session hijacking. To handle those issues, this paper promotes a single framework that combines behavioral biometrics, federated learning, as well as Zero Trust as the foundations of continuous authentication. This is because behavioral modalities like keystroke dynamics, mouse movements, gestures and motion signals give dynamic identity traits that are difficult to forge. Federated learning guarantees privacy protection because raw biometric data are only stored on user devices and that global model refinement is achieved using safe parameter aggregation. The system uses trust-scoring engine to dynamically scale access privileges in relation to live indicators of conduct and context-sensitive risk indicators. Comparison with benchmark datasets: HMOG, Buffalo Keystroke, and Touchalytics shows that the proposed framework is more accurate, with an Equal Error Rate (EER) of only 7 percent, lower False Acceptance and Rejection Rates, and real-time latency of less than 200 ms. A comparative study proves that both security and usability have become much more advanced than state of the art. The work can provide a scalable, versatile, and privacy-respecting next-generation continuous authentication solution, with potential practical use in finance, healthcare, and internet of things domains as well as defense.