Enhancing Federated Assessment via Sparse Fine-Tuning

This presentation delves into the challenges of non-IID data in Federated Learning (FL) and explores innovative solutions, including a proposed sparse fine-tuning approach. Using the ViT-S/16 model, experimental results are shared to evaluate and compare the effectiveness of this method, demonstrating its potential to enhance accuracy in FL assessments.