Towards_Scalable_and_Secure_Federated_Learning__Key_Issues_and_Future_Research.pdf

Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training across distributed data sources while ensuring data privacy. Unlike traditional centralized approaches, FL allows multiple clients (e.g., mobile devices, IoT sensors, and hospitals) to train a global model without sharing their raw data. This survey provides a comprehensive overview of FL, discussing its fundamental concepts, architecture, algorithms, and optimization techniques. We highlight the diverse applications of FL across various industries, such as healthcare, finance, autonomous vehicles, and smart cities, showcasing its potential to address real-world challenges related to data privacy, security, and scalability. Additionally, we identify the key research challenges and open issues in FL, including communication overhead, data heterogeneity, adversarial attacks, and regulatory compliance. Finally, we discuss the future directions of FL, focusing on potential advancements in communication efficiency, privacy-preserving techniques, and integration with emerging technologies such as edge computing and 5G networks. This survey aims to provide a thorough understanding of FL's current state and its promising future in shaping the landscape of distributed machine learning.