MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
Federated Learning (FL) enables distributed model training without centralizing data but suffers from high communication overhead. To address this, we propose Block-Wise Marginal Utility Contribution (MUC), a framework that selectively transmits only the most impactful data blocks based on their contribution to model performance. To evaluate our framework, we apply it to a multimodal dataset integrated from multiple MIMIC clinical datasets and show that only 24 out of 1,135 candidate blocks (1.76%) carry meaningful improvement signals, enabling a potential communication reduction of 45-50% while maintaining or improving model quality. Our deduplication-based block selection achieves a mac...
| Authors | Akshay Mhatre, Vikram Karthick, Deepti Gupta et al. |
|---|---|
| Primary category | cs.DC |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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