FL Framework

Federated learning (FL) frameworks aim to collaboratively train machine learning models on decentralized data, preserving data privacy. Current research focuses on improving FL efficiency and robustness by integrating techniques like integrated sensing, communication, and computation (ISCC), addressing non-independent and identically distributed (non-IID) data through clustering and optimized aggregation strategies, and enhancing privacy through differential privacy mechanisms. These advancements are significant for enabling large-scale collaborative machine learning while mitigating communication overhead and ensuring data security, with applications ranging from personalized healthcare to resource-constrained IoT devices.