## 5 Challenges
### Real-Time Processing and Energy Efficiency  
Many IoMT applications, such as detecting abnormalities in ECG or oxygen levels, require instantaneous responses. Ensuring ultra-low latency in processing and delivering predictions is technically challenging, especially in cloud-dependent setups. Devices, especially wearables and implants, operate on limited battery life. Continuous running of DL algorithms can drain energy quickly, limiting device functionality. Balancing model complexity and energy efficiency is a critical challenge. Real-time processing and energy efficiency are critical challenges for IoMT applications, such as detecting abnormalities in ECG patterns, oxygen levels, or vital signs. These use cases demand ultra-low latency to ensure timely interventions, but reliance on cloud-based architectures often introduces delays due to data transmission and processing. Additionally, wearable and implantable IoMT devices operate on limited battery life and computational resources, making the continuous execution of DL algorithms highly energy-intensive. Complex DL models, such as those with convolutional layers or transformers, provide high accuracy but are impractical for resource-constrained devices without optimization. Strategies to deal with these challenges include leveraging edge computing to perform local data processing and reducing latency and energy consumption. Model optimization techniques, such as 10 MT aggregation to pruning, quantization, and knowledge distillation, minimize computational demands while maintaining accuracy [244]. Federated learning enhances privacy and reduces communication overhead by enabling collaborative training without transmitting raw data to the cloud. For instance, [PERSON] et al. designed a federated split learning (FedSL) framework to collaboratively use healthcare analytics on a number of IoMT devices with limited resources [246]. [PERSON] et al. proposed to use FL to train a deep learning (DL) model to classify decentralized data from various IoMT devices [247]. Likewise, [PERSON] et al. proposed an IoMT framework named \"FedCare\" to add edge nodes to standardize the data to solve heterogeneity issues in federated learning [248]. Energy-efficient hardware, including specialized GPUs and TinyML platforms, along with adaptive algorithms that adjust model complexity based on resource availability, further improve efficiency. [PERSON] et al. presented an energy-efficient design approach for monitoring the heart from wearable devices continuously. According to their proposal, a hybrid edge-fog/cloud IoMT system is proposed to detect noise before transforming the data to save on the overall energy assumption [249]. [PERSON] et al. proposed to use the Coral Reef-Based Energy-Efficient Framework to save energy. In their research, a coral means a number of coded solutions. A special grid cell is used to store the data. If better data come out but no space is available in the reef, the new data will replace the old data. Such a method reduces the overall size to reduce the assumptions during the data processing [250]. Incorporating low-power communication protocols like Bluetooth Low Energy (BLE), and advancements in battery technology, such as solar-powered or rechargeable batteries, can extend device longevity. For example, [PERSON] et al. used BLE 5.0 on a wearable wristband to send data to the server [251]. However, BLE is subject to man-in-the-middle (MITM) attacks during the paring called \"Just Works\" which does not provide any I/O capabilities [252; 253]. In order to protect devices during the paring process, security methods such as ACL and blockchain, should be considered to add on top of the BLE 5.0. [PERSON] et al. proposed the Ephemeral Elliptic Curve Diffie-Hellman (EECDH) to obtain a session key to provide protection on the BLE5.0 [251]. Locality-sensitive hashing (LSH) is used to identify nearest neighbors in ML and DM to ensure only nearby data are reviewed.