The privacy-focused concept of Federated Learning (FL) allows local data processing without disclosing patients’ health details to a central server. However, its vulnerability to privacy breaches through shared model weights and susceptibility to a single point of failure remain concerns. Energy constraints of Wireless Body Area Networks (WBANs) necessitate considering computation and transmission energy in the FL process. Thus, this article introduces a smart healthcare system prioritizing energy efficiency and privacy through a blockchain-backed FL model. Yet, WBAN users might be unwilling to share data without adequate incentives, and miners might hesitate due to the high energy usage associated with maintaining the blockchain. Therefore, an optimization problem is formulated to maximize system utility while considering energy, WBAN incentives, miner revenue, and FL loss. A computationally efficient stable matching-based algorithm is proposed for optimizing utility via associating WBANs and miners. Associated WBANs use Quantized Neural Networks (QNNs) to minimize computation energy. Moreover, this work integrates Differential Privacy (DP) and Homomorphic Encryption (HE) mechanisms to prevent information leakage by adding noise to gradients before updating model weights and encrypting consequences before transmitting them to miners. Real-world experiments validate the framework, yielding an average of 15.1%, 9.03%, and 15.35% improvements over existing methods.

This work explores the coupling of Machine Learning (ML) and Wireless Body Area Network (WBAN) data to develop highly effective models. To support resource-constrained WBANs, we propose the integration of Drones-as-a-Service (DaaS) for on-demand data collection and model training. However, the growing number of WBAN users with varying 5G radio resources may cause interference and degrade system performance when transmitting data to Unmanned Aerial Vehicles (UAVs), hindering data sharing among independent UAVs. To address these challenges and enable privacy-preserving collaborative ML, we adopt Federated Learning (FL) framework, enabling independent UAV service providers to collaborate without sharing sensitive data. Furthermore, we aim to maximize the revenue of both WBANs, which contribute data, and UAVs, which perform model training. This requires careful resource allocation, considering minimum and maximum Physical Resource Block (PRB) requirements for transmitting critical and complete physiological data to UAVs underlying 5G networks. To tackle this complex problem, we propose an optimization framework that maximizes overall revenue while considering interference among WBANs. We apply stable matching and graph coloring-based heuristics to solve the problem efficiently. Extensive simulations and real-world data prototype demonstrate our proposed model's effectiveness, achieving an average revenue of 92.8% of the optimal value, outperforming existing state-of-the-art approaches.

Growing remote health system allows continuous monitoring of patients' conditions outside medical facilities. However, the real-time smart-healthcare applications having latency limitations, must be solved efficiently. Fog computing is emerging as an efficient solution for such real-time applications. Therefore, Medical Centers (MCs) are becoming more interested in offering IoT-based remote health monitoring services to get profited by deploying fog resources. However, an efficient algorithmic model for allocating limited fog computing resources in a criticality-aware smart-healthcare system while considering the profit of MCs is needed. Thus, we formulate an optimization problem by maximizing system utility, calculate as a linear combination of MC's profit and patients' cost together. We propose a flat-pricing based scheme to measure the profit of MC in health monitoring system. Further, we propose a swapping-based heuristic to maximize the system utility. The proposed heuristic is evaluated on various parameters and shown to be closed to the optimal while considering the criticality of patients and the profit of MC, together. Through extensive simulations, analysis on real-world data and prototype implementation, we find that the proposed heuristic achieves an average utility of 94.5% of the optimal, in polynomial time complexity.