Federated learning with adaptive reliability aware aggregation for privacy preserving healthcare IoT systems

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Healthcare Internet of Things (IoT) devices generate vast amounts of sensitive patient data, creating significant privacy risks and regulatory challenges when processed through centralized architectures. Federated Learning (FL) offers a promising solution by enabling decentralized model training without sharing raw data. However, real-world deployment faces critical challenges including device heterogeneity, intermittent connectivity, non-independent and identically distributed (non-IID) data, and unreliable node participation. This paper proposes a Reliability-Aware Adaptive Aggregation (RAAA) framework that dynamically evaluates client trustworthiness through three dimensions: update quality (local accuracy and gradient alignment), participation consistency (historical engagement patterns), and communication latency. These metrics are integrated into a composite reliability score using weighted aggregation, which adaptively prioritizes contributions from trustworthy nodes during global model updates. The framework was validated using PhysioNet electrocardiogram (ECG) datasets (MIT-BIH and PTB) with 20 simulated healthcare IoT clients under conditions of node dropout and data heterogeneity. Experimental results demonstrate that RAAA achieved 91.7% classification accuracy on arrhythmia detection, outperforming Federated Averaging (FedAvg) (85.2%), Federated Proximal (FedProx) (86.4%), and Q-FedAvg (87.1%) by 6.5%, 5.3%, and 4.6%, respectively. The proposed method converged 17% faster (43 versus 52 rounds) and maintained an 85.6% client participation rate. Cross-dataset validation on the PTB database confirmed consistent improvements across different physiological signals. The RAAA framework provides a robust, privacy-preserving FL solution for healthcare IoT environments with superior accuracy, faster convergence, and resilience to unreliable nodes. All evaluations are conducted in a simulated environment; while theoretical energy and complexity analyses suggest suitability for resource-constrained edge devices, real-hardware validation remains an important direction for future work.