MUC-FL：基于分块边际效用贡献的通信高效联邦学习

中文摘要 AI 辅助

联邦学习（FL）能够在无需集中数据的情况下进行分布式模型训练，但面临高昂的通信开销。为解决这一问题，我们提出了分块边际效用贡献（MUC）框架，该框架根据数据块对模型性能的贡献，仅选择性传输最具影响力的数据块。为评估我们的框架，我们将其应用于由多个MIMIC临床数据集整合而成的多模态数据集，结果显示1,135个候选块中仅有24个（1.76%）携带显著的改进信号，从而在保持或提升模型质量的同时，可实现45-50%的通信量削减。我们基于去重的块选择方法取得了0.8566的宏F1分数，而标准联邦优化仅为0.8155，表明选择性传输能够提升性能，尤其是在代表性不足的类别中。

英文摘要

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.

发表机构

• Arizona State University（亚利桑那州立大学）

机构由 AI 辅助整理，请以论文原文为准。