## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.2 Dimension II: Data Volume  
The data volume challenge is a key aspect of AI research and application. Large datasets are critical for training AI models, and continue to grow in size and complexity. This growth brings with it some challenges that must be addressed to ensure the effective use of AI in various domains [71]. The challenging elements of data volume are presented in Figure 8.  
Data Deluge: A double-edged sword. Exponential data growth is the driving force behind the success of AI, particularly in deep learning techniques [72]. However, the massive amount of data poses several challenges, including in storing, processing, and managing data [73]. Storage Challenges: The huge amount of data generated today requires more efficient storage solutions to support artificial intelligence applications [74]. Traditional storage architectures may not be able to meet the scalability, performance, and cost requirements of AI workloads [75]. New storage technologies, such as nonvolatile memory (NVM) and distributed storage systems, have been proposed as possible solutions [76]. Processing Challenges: AI models, particularly deep learning algorithms, require enormous computing resources to process large datasets [77]. This has led to an increased need for specialized hardware such as GPUs and TPUs to accelerate AI training and inference [78]. In addition, new techniques such as model compression, pruning, and quantization have been explored to optimize AI models for more efficient processing [79]. Data Management Challenges: From the perspective of big data volume, effective data management is critical for AI systems to handle massive amounts of data. This includes data cleaning, preprocessing, labeling, and curation [80]. Techniques such as active learning, weak supervision, and transfer learning have been proposed to alleviate the burden of manual data annotation [81]. Data Heterogeneity: Large datasets may contain data from multiple sources, which can be challenging to integrate and harmonize, particularly when the data are in different formats or structures. Data Privacy and Security: Large data volumes can increase the risk of data breaches and privacy violations, particularly when sensitive data are involved. These issues need to be addressed as the amount of data increases [82]. Bias and Representativeness: Large volumes of data do not necessarily guarantee representativeness or lack bias, as they may still contain demographic, cultural, or other biases that can impact the accuracy of AI models. Data Access: In some cases, organizations may have access to large datasets but may not be able to use them due to legal or regulatory constraints. Organizations must ensure that they have the necessary permissions and licenses to access and use data. Figure 8: Challenging elements of data volume. Mitigation Solutions for Challenges of Data Volume  
Several solutions have been proposed to address the data volume challenges in artificial intelligence. Figure 9 depicts the proposed solutions that this includes. 1. Transfer Learning: Transfer learning involves leveraging pre-trained AI models to improve the performance of new models. Using pre-trained models, organizations can reduce the amount of training data required and improve the efficiency of the training process. 2. Federated Learning: This enables collaborative model training across multiple devices without sharing raw data [83]. 3. Edge Computing: This brings data processing closer to the data source, thereby reducing the network latency and bandwidth usage [84]. 4. Multimodal Learning: This leverages multiple data sources to improve the model performance and reduce reliance on large datasets [85]. Federated Learning as a proposed solution:  
Federated learning was introduced as a solution to preserve user privacy while benefiting from the collective knowledge of multiple data sources [86]. In this framework, devices (also known as clients) train machine learning models using local data, and then share model updates with a central server.