MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
This work proposes Block-Wise Marginal Utility Contribution (MUC), a framework that selectively transmits only the most impactful data blocks based on their contribution to model performance, and applies it to a multi-modal dataset integrated from multiple MIMIC clinical datasets.
Akshay Mhatre, Vikram Karthick, Deepti Gupta et al.
· IEEE International Conferenc... · 1 citation