Federated learning for secure and privacy preserving data analytics in heterogeneous networks

Federated Learning (FL) has emerged as a groundbreaking paradigm enabling collaborative machine learning across distributed nodes without centralizing data, thus addressing critical concerns in security and privacy. This survey explores the application of FL for secure and privacy-preserving data analytics in heterogeneous networks, where diverse devices, data distributions, and network conditions present unique challenges. This paper provides a comprehensive review of recent advancements in FL, focusing on its efficacy in safeguarding sensitive information while enabling effective analytics across varied domains such as healthcare, finance, and IoT systems. The paper delves into key methodologies for achieving privacy preservation, including differential privacy, secure multi-party computation, and homomorphic encryption, while analyzing their performance in dynamic and resource-constrained environments. Additionally, this paper examines strategies for managing heterogeneity, including personalized FL, model aggregation techniques, and adaptive optimization algorithms. Challenges such as scalability, communication efficiency, and adversarial robustness are discussed alongside potential solutions and future research directions. This survey aims to provide researchers and practitioners with an in-depth understanding of the state-of-the-art in FL for secure and privacy-preserving data analytics, fostering innovation and addressing emerging needs in increasingly complex network ecosystems.