# Centralized vs. Decentralized Cloud Computing in Healthcare
## 6 Decentralized Cloud Computing
[69] discussed how healthcare systems might integrate with distributed cloud storage and hybrid image compression. The hybrid image compression technique, combining lossless and lossy compression, helps reduce the data size and transmission bandwidth requirements, leading to faster data retrieval and transfer, which leads to improved system performance. With the suggested appliance, it underlines the importance of data compression in reducing redundancy and saving storage space and bandwidth. While hospitals employ teleraliology to diagnose X-rays remotely, the transmission of a 10 MB image using a high-speed modem during emergencies can take up to half an hour. Therefore, compression not only concerns storage costs, but also involves transmission time and the utilization of imaging apparatus. The hybrid image compression technique reduces storage requirements, leading to cost savings in cloud storage. Combining distributed cloud storage and hybrid image compression can help healthcare organizations optimize their data storage and management costs. Yet, the paper does not discuss specific mechanisms for ensuring patient data privacy in detail, such as encryption, access control, and data segregation. The authors of [70] discussed the advantages of utilizing distributed cloud technology in mobile healthcare systems to improve data storage and organization, proposing a Distributed Data Analytics and Organization Model (DDAOM) to minimize errors and inefficiencies involved in managing large amounts of data. The model uses federated learning to perform calculations for multiple services with different input and output data assignments. This method reduces delays in storage and processing, as well as varying service provision and backlogs. By managing state maintenance through realistic learning iterations, the model ensures the smooth deployment of services, reducing latency. In federated learning, past states' memory is utilized to allocate local input from different edges Through decentralized processing paradigms, ensuring the confidentiality of health-related personal information and preventing mining in certain areas to better protect sensitive patient information. Federated learning also allows for decentralized storage and processing, eliminating the need for a centralized and expensive infrastructure. An empirical evaluation of the DDAOM demonstrated enhancements in data retention latency, analytical processing speed, service dissemination efficacy, task queue management, and resource distribution equilibrium. Specifically, it achieved 9.86% less storage delay, 10.72% less mining delay, a 10.76% higher service distribution, 9.45% fewer backlogs, and a 8.4% higher allocation ratio than the Mobility-aware Task Scheduling and Allocation Approach (MobMBAR), retrieval and storage-based indexing framework (RSIF), and Effective Training Scheme for Deep Neural Networks (ETS DNN). However, relying on federated learning and state management techniques may introduce inherent latency. As [71] highlighted, the healthcare industry faces significant challenges in the management and processing of large amounts of data using traditional database management systems. The authors proposed a solution to manage both structured and unstructured data effectively by combining the scalability of non-relational databases (NoSQL) services with the capabilities of a Relational Database Management System (RDBMS). The RDBMS is utilized for the efficient management of structured data by offering robust querying and transaction capabilities, ensuring optimal performance for structured data operations. NoSQL databases are used to manage unstructured and semi-structured data. They are highly scalable and provide low-latency access, which is essential for handling significant quantities of health-related digital assets encompassing diagnostic imagery, auditory records, and video graphic documentation. By combining RDBMS and NoSQL, the model can achieve improved overall system response time and latency.