Local Client Model

Local client models are a crucial component of federated learning (FL), aiming to enable collaborative model training across distributed devices while preserving data privacy. Current research focuses on improving efficiency and robustness by addressing challenges like client unavailability, heterogeneous model architectures, and limited communication bandwidth, employing techniques such as adaptive optimization algorithms (e.g., Adam-based methods), efficient knowledge distillation with minimal shared data, and novel aggregation strategies to handle varying client participation. These advancements enhance the practicality and scalability of FL, with implications for diverse applications ranging from personalized mobile health to large-scale distributed AI systems.