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arXiv Machine Learning By Akshay Mhatre, Vikram Karthick, Deepti Gupta, Jia Zou

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

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The paper introduces MUC-FL, a block‑wise marginal utility contribution framework that selectively transmits only the most impactful data blocks in federated learning to reduce communication overhead. Applied to a multimodal dataset derived from multiple MIMIC clinical datasets, the method identifies 24 out of 1,135 candidate blocks (1.76%) as carrying meaningful improvement signals, potentially cutting communication by 45‑50% while preserving or enhancing model quality. The deduplication‑based block selection achieves a macro F1 score of 0.8566 versus 0.8155 for standard federated optimization, showing improved performance especially for underrepresented classes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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