## 6 Conclusions  
This paper addressed the critical challenges associated with ML for sustainable development and introduced a novel communication-efficient FL framework, FedLO, designed to enhance scalability and sustainability in resource-constrained environments. The pressing need for sustainable solutions necessitated innovative data-driven tools, and FL emerged as a transformative paradigm by decentralizing ML training to edge devices. However, communication bottlenecks hindered its scalability. The proposed FedLO framework leverages the power of the LO, a nature-inspired metaheuristic algorithm, to strategically select relevant model updates, significantly reducing communication overhead. The key contributions of this work include the introduction of the FedLO framework, which achieved a significant communication cost reduction compared to existing methods while maintaining high model accuracy. Through rigorous evaluation of diverse datasets representing various areas of sustainable development, FedLO demonstrated its superiority over other FL approaches. The experiments showcased FedLO's exceptional accuracy, scalability, and sustainability performance, making it a promising solution for real-world sustainable development initiatives. The study delved into the dynamics of model training using FedLO on the rice leaf disease and waste recycling plant datasets, providing insights into the algorithm's effectiveness in progressively refining model performance over multiple training epochs.  
Moreover, the robustness of FedLO was demonstrated in an unstable network environment, where random data drops were introduced. FedLO consistently outperformed other FL algorithms, maintaining higher accuracy under varying failure rates. However,FedLO has limitations and areas where its performance can be enhanced. These include its adaptability to different FL scenarios, optimization efficiency, and robustness in unstable network environments.  
To address these limitations and enhance FedLO's performance, several avenues for future research are proposed:  
* Algorithmic Modifications for LO: Investigating and implementing modifications to the LO algorithm could be explored to improve its efficiency and adaptability to various FL scenarios. Fine-tuning the parameters or introducing novel strategies may lead to further performance gains.
* Hybridization with Other Optimization Algorithms: Exploring hybrid approaches by combining LO with other optimization algorithms could offer a synergistic effect, leveraging the strengths of different algorithms. Hybridizing LO with popular optimization techniques might enhance the overall performance and robustness of the FedLO framework.
* Dynamic Adaptation of Communication Strategies: Developing dynamic communication strategies within FedLO that adapt to the changing network conditions could be crucial. Investigating methods to dynamically adjust the communication frequency or prioritize specific updates based on network stability can improve the framework's resilience in fluctuating environments.  
Conceptualization, M.A.A.-B. and A.K.A.; methodology, M.A.A.-B., Z.A.A.A. and A.K.A.; software, M.A.A.-B. and A.K.A.; validation, M.A.A.-B. and A.K.A.; investigation, M.A.A.-B.; resources, M.A.A.-B., S.F. and R.F.M.; writing--original draft, M.A.A.-B., A.K.A., Z.A.A.A., S.F. and R.F.M.; Visualization, M.A.A.-B., A.K.A., Z.A.A.A., S.F. and R.F.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grant from the Deanship of Graduate Studies and Research (DGSR) at Ajman University, Ajman, UAE (Grant No. 2023-IRG-ENIT-15).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.