## 7 Future Directions  
As part of our ongoing efforts to enhance FL systems, we will focus on several key areas of development that align with the evolving challenges in the field. Our future research will concentrate on the following advancements:  
**Integrating Statistical Heterogeneity and Optimizing Edge Reputation in FL:**  
In our future work, we aim to incorporate statistical heterogeneity into our resource-aware federated profiling framework, drawing inspiration from methodologies in studies such as Oort [47] and PyramidFL [48]. This will enhance our model's adaptability in environments with diverse data distributions across nodes. Simultaneously, we plan to optimize the edge reputation factor, which assesses the historical performance and contribution of nodes. This optimization will include integrating advanced utility profiling mechanisms that adapt to the changing performance and contributions of nodes over time, ensuring more effective and reliable node selection.  
**Addressing Computational Stragglers in Federated Learning:**  
We intend to tackle the challenge of computational stragglers, employing strategies such as AD-PSGD algorithm [49] and Prague [50], for efficient information exchange and robust asynchronous operations. AD-PSGD algorithm [49], aims to enhance the overall efficiency, particularly in dealing with nodes that exhibit slower or irregular computational performance. The Prague algorithm [50], a high-performance, heterogeneity-aware training method that enhances synchronization efficiency and reduces conflicts, using advanced techniques like Partial All-Reduce and static group scheduling make it an ideal model for optimizing federated learning systems and managing computational stragglers.  
**Expanding Experimentation with Diverse Datasets:**Our experiments are currently limited to a specific, homogeneous dataset, highlighting the need for future research to include more varied and heterogeneous data to comprehensively assess the proposed design's effectiveness and scalability in diverse real-world scenarios. To validate and refine our approach, in the future we will employ a range of datasets in our experiments. This will enable us to assess the robustness and scalability of our design under different conditions and conduct comprehensive comparisons with state-of-the-art resource optimization strategies and systems.  
**Enhancing Fine-grained Client Selection:**  
We will also explore fine-grained client selection strategies to improve time-to-accuracy performance in FL systems. A key technique we plan to incorporate is client clustering based on data characteristics. This method involves grouping clients with similar data distributions or computational capabilities, allowing for more targeted and efficient model updates. This approach will focus on exploiting data and system heterogeneity within selected clients for more efficient utility profiling, leading to improved model training efficiency and effectiveness.  
In addition to the areas mentioned, further research is critical to fully evaluate the efficacy and scalability of our system in real-world scenarios, particularly in addressing privacy and security concerns. As improving node collaboration and resource utilization remains a top priority, future developments will focus on enhancing the FWP to allow for resource offloading to resource-rich nodes. By doing so, we aim to maximize the efficiency of resource allocation across the network, ensuring optimal utilization of available resources while maintaining system integrity and security.