Lightweight Client

Lightweight clients in federated learning aim to enable resource-constrained devices, like mobile phones and IoT sensors, to participate in collaborative model training without compromising data privacy or incurring excessive computational costs. Current research focuses on techniques like model splitting, transfer learning, and knowledge distillation from larger foundation models to improve accuracy and efficiency, often addressing challenges posed by heterogeneous data distributions and limited client resources. These advancements are crucial for expanding the reach of federated learning to a wider range of applications and devices, particularly in areas like personalized medicine and IoT-based services.