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papersSEP 11 04:00 UTC

MUC-FL method cuts federated learning communication by sending only high-value blocks

Researchers propose Block-Wise Marginal Utility Contribution (MUC), a scheme for federated learning that decides which parts of a model update are worth transmitting. By estimating the marginal utility of each block, the framework aims to reduce the communication overhead that typically limits distributed training. The work is published as an arXiv preprint.

arXivBlock-Wise Marginal Utility Contribution (MUC)MUC-FLCommunication Efficiencydistributed trainingfederated learning

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGMUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning ↗SEP 11 04:00 UTC

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