The rapid digitalization of healthcare systems has generated vast volumes of biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and photoplethysmograms (PPG). While these data streams offer unprecedented opportunities for early disease diagnostics, their collection and centralized processing raise severe concerns about patient privacy, data ownership, and regulatory compliance. Traditional machine learning pipelines require the gathering of raw data, increasing potential for data leaks, and violating confidentiality policies. Federated learning (FL) is a very attractive approach to eliminate the functionality of a centroid and instead allowing training to occur collectively across disparate health care nodes, while still ensuring raw patient data does not leave the medical facility. However, while federated learning has exciting potential as a paradigm, there are several challenges to its traditional implementation including; potential communication overhead, model divergence when several players are exposed to the same material from a population, and limited robustness to adversarial attacks. To address these challenges, this paper proposes a federated diagnostic framework which protects patient privacy that combines biomedical signal preprocessing, differential privacy, homomorphic encryption, as well an optimized aggregation scheme meant for non-IID clinical data. The novelty lies in embedding adaptive personalization layers at local nodes while maintaining a globally consistent diagnostic backbone. Experimental evaluation was performed on benchmark biomedical datasets (MIT-BIH Arrhythmia, Sleep-EDF, and PhysioNet EEG Motor Movement) under cross-institutional simulation. The framework achieved an accuracy of 94.7% in arrhythmia detection, 93.1% in seizure classification, and maintained <2% accuracy drop when differential privacy was enforced with ε = 1.0. Communication latency was reduced by 27% compared to FedAvg, and robustness improved against model inversion attacks with >40% resilience margin. These results confirm the framework’s feasibility for real-world privacy-preserving healthcare diagnostics, bridging the gap between diagnostic accuracy and regulatory compliance.