Loss Aware Federated Learning for Service Migration in Multimodal E-Health Services

In an emergency healthcare situation, delay between injury and treatment is one of the most critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by processing real-time ambulance data while en route to hospital can cut back the delay in treatment of the patient. However, several research challenges arise in accessing real-time patient data from ambulance to hospital while moving along different Road Side Units (RSUs). Due to the severity of medical data, there is a need to minimize computational losses along with costs due to migration and ambulance perceived latency. Considering the above scenarios, this paper formulates an average cost minimization problem keeping latency, energy, and loss function into deliberation as NP-hard. To solve the formulated problem, Minimum Cost Algorithm (MCA) using Federated Averaging (FedAvg) algorithm utilizing RSUs for effectively transferring real-time patient data to hospitals has been proposed considering above stated constraints altogether. Moreover, to handle imbalances in health data across different hospitals during processing, FedAvg algorithm combines augmentation techniques. Through experimental and prototype demonstration, the efficacy of proposed framework is shown by achieving 12.5%, 27%, and 38% reduction in an average total cost compared to other state-of-the-art techniques on real-world data sets, respectively.