MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning — Paper

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.

Authors 4

Akshay Mhatre, Vikram Karthick, Deepti Gupta, Jia Zou

Specification

Official page: arxiv.org/abs/2609.10545
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2609.10545
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.DC, cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2609.10545
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.DC
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

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9

Source tiers

T19

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1 h ago

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Authors: Akshay Mhatre, Vikram Karthick, Deepti Gupta

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperMUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
New paper: MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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