Continuous, remote patient monitoring is now practically possible thanks to the expanding use of wearable and bedside sensors enabled by the Internet of Things (IoT). However, the centralised aggregation of physiological data needed by traditional deep learning pipelines presents significant privacy, legal, and bandwidth issues. In order to build a shared diagnostic model across dispersed IoT healthcare nodes without sending raw patient data to a central server, this study suggests a federated deep learning (FDL) system. The framework integrates a FedAvg/FedProx aggregation scheme at the server with a lightweight hybrid convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) architecture for local physiological-signal feature extraction. Differential privacy (DP) noise injection and secure aggregation are added to prevent information leakage from shared gradients. We present a simulation-based evaluation intended to characterise the expected accuracy, communication-efficiency, and privacy-utility trade-offs of the framework in comparison to centralised and local-only baselines. We also describe the end-to-end system architecture, the on-device training and communication protocol, and the privacy-accounting method. The results show that the suggested federated strategy can reduce per-round communication volume by an order of magnitude, eliminate the need to transmit raw sensor data, and approach centralized-training accuracy within a narrow margin. We also examine how the differential-privacy budget affects model utility and talk about unresolved issues with adversarial robustness, device and network heterogeneity, and statistical heterogeneity (non IID data). The suggested approach provides a workable blueprint for scalable, privacy preserving, and regulator-compliant AI-based healthcare monitoring at the network edge.

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