Optimizing Federated Learning: Addressing Key Challenges in Real-World Applications

Federated learning (FL) has emerged as a powerful solution for training machine learning models across decentralized data sources, preserving privacy by keeping data on local devices. However, the method introduces significant challenges, particularly in managing asymmetric client availability, addressing statistical and system heterogeneity, and maintaining privacy while optimizing model performance. In this study, we critically examine these challenges and present a systematic taxonomy that categorizes FL research into key areas: optimization algorithms, client selection strategies, heterogeneity handling, and privacy preservation. We analyze how balancing these aspects is essential for the successful deployment of FL in real-world applications. Additionally, we explore future research directions and provide practical insights into how FL can be optimized and applied in industries such as healthcare, energy, and autonomous systems. This survey offers a comprehensive resource for researchers and practitioners seeking to enhance the robustness, fairness, and efficiency of FL systems in diverse and dynamic environments.