## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.2 Dimension II: Data Volume
The server aggregates these updates, improves the global model, and distributes the updated model back to the clients. This process is repeated iteratively until the model converges. The benefits of Federated Learning include several benefits that can help address data volume challenges in AI. 1. Privacy: Because raw data remain on client devices, federated learning inherently provides a higher level of privacy compared to centralized approaches [83]. 2. Reduced Data Transfer: By sharing only model updates rather than raw data, federated learning can significantly reduce the amount of data that needs to be transferred over the network, reducing bandwidth and latency issues [87]. 3. Scalability: Federated learning can accommodate a large number of client devices, allowing the use of different data sources without overloading the central server [75]. 4. Real-time learning: By allowing clients to learn from local data, federated learning enables real-time adaptation and improves model performance [82]. Figure 9: Proposed solutions for challenges of data volume. Challenges and Future Directions: Despite its benefits, federated learning also presents some challenges that need to be addressed. 1. Heterogeneity: The heterogeneity of client devices and data distribution may lead to an unbalanced contribution to the global model, which may affect convergence and model performance [75]. 2. Communication Overhead: The iterative process of exchanging model updates incurs significant communication overhead and may negate the benefits of reduced data transfer [87]. 3. Security: Federated learning is vulnerable to various security threats, including model poisoning, inference attacks, and Sybil attacks [88]. To overcome these challenges, researchers are exploring various techniques such as weighted averaging to deal with heterogeneity [89], communication-efficient algorithms for reducing overhead [90], and differential privacy for enhancing security [91]. Continued research in these areas will be crucial to fully realize the potential of federated learning in addressing the data-volume challenge in AI. * Edge Computing as a Proposed Solution: Edge computing is a distributed computing paradigm that aims to bring computation and data storage closer to the data source, or the \"edge\" of the network, where the data are generated [84]. By performing data processing on edge devices, such as smartphones, IoT devices, or edge servers, edge computing can reduce the amount of data that must be transmitted to the cloud or a centralized data center. This approach enables real-time data processing, reduces latency, and conserves the bandwidth. * Advantages of Edge Computing: Edge computing offers several benefits that can help address the data volume challenge in AI. 2. Reduced Latency: By processing data closer to the source, edge computing can significantly reduce latency and enable real-time AI applications [84]. 3. Bandwidth Efficiency: Edge computing helps conserve bandwidth by reducing the amount of data transmitted over the network, which is particularly useful in situations where the network bandwidth is limited or expensive [76]. 4. Enhanced privacy and security: Because data are processed and stored locally, edge computing can provide improved data privacy and security compared to centralized approaches [92]. 5. Scalability: Edge computing can support many devices and applications, making it suitable for the growing demands of AI and IoT [84]. Challenges and Future Directions:  
Despite its advantages, edge computing presents some challenges that need to be addressed. 1. Resource Constraints: Edge devices typically have limited computational resources, which may hinder the performance of complex AI models [93]. 2. Model Deployment and Management: Deploying and managing AI models across a large number of edge devices can be challenging because it requires efficient model distribution, updates, and monitoring [94]. 3.