## 5 Model Challenges and Feasible Solutions  
1. **Security and Privacy Challenges** The proposed model is concerned with maximizing resource usage and selecting the optimal edge nodes, but it also presents possible privacy concerns related to sharing sensitive information about the resources of the edge nodes. To address these concerns, the proposed model must implement robust security measures to protect the sensitive resource information from unauthorized access or misuse. For  
Figure 3: Edge Workload Profile. example, the model could use encryption techniques to protect the data during transmission and storage. Access controls can also be implemented to restrict access to the resource's information to authorized parties only. Additionally, the model must be transparent about its data handling and sharing practices, providing clear and concise privacy policies and obtaining informed consent from users before collecting and sharing their data. The FWP model's design incorporates a reputation-based system that assigns a reputation score to each node based on its past behavior. Nodes with a low reputation score are then excluded from the federated learning model. Another approach is to use techniques such as differential privacy or, more specifically FL enabled personalized differential privacy [41] to protect the privacy of the data, even in the presence of malicious nodes. Differential privacy adds noise to the data before sharing it with the nodes, which makes it difficult for malicious nodes to identify the data of a specific node. Other approaches include blockchain-based [42, 43] privacy information security sharing schemes. Overall, it is important to balance the benefits of the proposed model's resource optimization with the potential privacy risks, ensuring that adequate measures are in place to protect sensitive data and respect the users' privacy rights. This not only applies to FL, but also to any distributed or collaborative approaches that require data sharing and processing at the edge nodes. **Communication Overhead**  
If the edge nodes continuously update their resource information to an Edge Workflow Platform (EWP), it may incur additional communication costs. However, this can be addressed by optimizing the communication protocol and minimizing the frequency of updates. For example, the edge nodes can send updates only when there is a significant change in their resource availability or utilization. There is a risk that an edge node may provide incorrect or misleading information about its resources. This can happen due to various reasons such as hardware or software failures, malicious attacks, or incorrect configurations. There are several approaches that can be taken in such situations but not limited to:  
**Resource verification:** The EWP should verify and validate the resources before accepting the resource information from an edge node. **Data encryption:** To prevent malicious attacks on the data, edge encrypts its data before sending them to the server. The server can then decrypt the data and verify its authenticity. It is important to note that in the current context, the encryption is intended for resource profile data, not health datasets, and is focused on protecting personally identifiable information or other sensitive details related to the resources. **Model aggregation:** The server can use a weighted averaging technique to aggregate the updates from all the participating edges. This method assigns more weight to information received from nodes with a better reputation and less weight to updates from edges that have previously sent erroneous data. **Node selection:** The server will dynamically select edge nodes based on their performance history. Accordingly, nodes with a history of sending unreliable data can be excluded from the training process. **Computational Overhead**  
Given that we are only processing the quality profile and not the entire dataset, the computational overhead remains constant regardless of the size of the data.