通过优化本地批量大小，提升联邦学习收敛速度。
Collaborative Batch Size Optimization for Federated Learning
- 采用贪心随机搜索法自动调整各参与方的本地批量大小。
- 相比默认设置，收敛速度显著加快，性能接近手动调优。
- 适合资源异构的联邦学习场景，尤其在设备算力不均时有效。
联邦学习（FL）是一种去中心化的协作机器学习框架，可在不集中收集数据的情况下训练模型，已广泛应用于医疗诊断、金融反欺诈等领域。本文聚焦于通过硬件使用优化改进本地训练过程。尽管联邦参与方可能共享计算硬件，但由于彼此间无信息交换，不当的训练配置会阻碍训练效率。利用联邦学习固有的并行特性，我们采用贪心随机搜索算法，为所有参与方优化本地批量大小以获得最佳训练设置。实验结果表明，与默认参数设置相比，该方法显著提升了收敛速度，且性能几乎与本地参数手动优化的情况相当。
原文摘要 · Abstract (English)
Federated Learning (FL) is a decentralized collaborative Machine Learning framework for training models without collecting data in a centralized location. It has seen application across various disciplines, from helping medical diagnoses in hospitals to detecting fraud in financial transactions. In this paper, we focus on improving the local training process through hardware usage optimization. While participants in a federation might share the hardware they are training on, since there is no information exchange between them, their training process can be hindered by an improper training configuration. Taking advantage of the parallel processing inherent to Federated Learning, we use a greedy randomized search to optimize local batch sizes for the best training settings across all participants. Our results show that against default parameter settings, our method improves convergence speed while staying nearly on par with the case where local parameters are optimized.
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