Adaptive Federated Optimization

Federated Learning (FL) has emerged as a revolutionary technique for distributed machine learning for training a model on shared data without sharing the data itself. Nevertheless, privacy-related concerns and scalability difficulties remain a problem. This paper discusses the state-of-the-art works to improve the privacy and convergence at FL frameworks for targeted healthcare and financial applications, as well as smart devices. It focuses on methodologies that preserve user privacy, such as differential privacy, homomorphic encryption, secure multi-party computation, and methods that enhance the model’s efficiency, including model compression, communication optimization, and adaptive optimization algorithms. To overcome these challenges, this study helps in the future design of FL systems for vital domains with high scalability.