## 6 Final Considerations and Future Directions  
Advantages. The use of containers in a federated learning environment provides numerous benefits. Containers facilitate the easy deployment and management of applications by encapsulating all the necessary dependencies and configurations. This ensures consistency across different environments, reducing the chances of conflicts and errors. Furthermore, containers support rapid scaling and resource allocation, which is critical in handling the dynamic workloads typical of federated learning systems. The isolation provided by containers also enhances security by limiting the potential impact of vulnerabilities within any single container.  
Disadvantages. Despite their benefits, containers also present some challenges. One of the main disadvantages is the complexity involved in managing a large number of containers, especially in distributed environments. This requires sophisticated orchestration tools like Kubernetes, which can have a steep learning curve. Additionally, while containers improve resource utilization, they still introduce overhead compared to running applications directly on the host OS, potentially affecting performance. The security of containers is an ongoing concern, as misconfigurations or vulnerabilities can lead to breaches if not properly managed.  
Unresolved Issues. Several unresolved issues remain in the deployment of federated learning using containers. One significant challenge is ensuring data privacy and security across distributed nodes, particularly when dealing with sensitive information. While encryption and secure communication protocols help, they do not eliminate all risks. Another issue is the heterogeneity of participating devices, which can vary widely in terms of computational power, connectivity, and data quality. Balancing the load and ensuring fair contribution from all nodes is an ongoing area of research. Additionally, managing the orchestration of containers in highly dynamic environments with frequent changes in node availability remains complex. Also, increasing the performance of the presented model can be challenging due to the distribution of data, leading to undesirably low performances, compared to the centralized architecture.  
Further Developments. Looking ahead, several developments could enhance the deployment and effectiveness of federated learning in containerized environments. One promising area is the use of edge computing, where computation is performed closer to the data source, reducing latency and bandwidth usage. Integrating advanced AI and machine learning techniques to optimize resource allocation and orchestration decisions could also improve efficiency and performance. Additionally, developing more robust security frameworks specifically designed for federated learning can address some of the current privacy and security challenges. The evolution of federated learning algorithms that can better handle heterogeneous and dynamic environments, such as those incorporating reinforcement learning for adaptive learning rates and model updates, is another crucial area. Finally, enhancing interoperability standards among different container orchestration platforms can simplify the integration of various systems and improve overall flexibility.  
Another important aspect of the use of cluster management platforms to tackle this problem is automatic scaling. In this paper, we presented an automated scaling platform based on Kubernetes, which we also attached to a custom metric. This enabled us to automatically scale the number of nodes--containers in this case--based on the required precision. This could lead to a cost-effective platform for federated learning, which is currently being studied and developed by the team.