aiqbench analysis
This research introduces MUC, a framework for Federated Learning that drastically cuts communication overhead by transmitting only the most impactful data blocks. Tested on integrated clinical datasets, the method achieves significant bandwidth savings while improving model performance on underrepresented classes compared to standard optimization. The work advances the efficiency of distributed machine learning systems without centralizing sensitive data.
MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
arXiv:2609.10545v1 Announce Type: cross Abstract: 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 macro F1 score of 0.8566 compared to 0.8155 for standard federated optimization, demonstrating that selective transmission can improve performance, particularly in underrepresented classes.
Abstract: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 macro F1 score of 0.8566 compared to 0.8155 for standard federated optimization, demonstrating that selective transmission can improve performance, particularly in underrepresented classes.
Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
arXiv:2609.10545 [cs.DC]
(or arXiv:2609.10545v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2609.10545 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 216 words
