通过数据纯度加权提升联邦学习收敛速度
FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting
- 根据设备数据的信息量计算贡献度，动态调整模型聚合权重
- 在EMNIST和CIFAR-10上比基准方法减少超40%通信轮次
- 适用于数据分布极不均衡的场景，适合实际部署
联邦学习（FL）是一种多方协作训练全局模型同时保护本地数据隐私的范式。其主要挑战在于设备间数据非独立同分布（non-IID），导致训练效率低、收敛慢。为此，我们提出联邦纯度加权（FedImp）算法，基于本地数据的信息内容量化各设备贡献，并归一化后计算差异化的全局模型更新权重。在EMNIST和CIFAR-10数据集上的大量实验表明，相比FedAvg、FedProx和FedAdp，FedImp在EMNIST上分别减少64.4%、27.8%和66.7%的通信轮次，在CIFAR-10上分别减少44.2%、44%和25.6%。在高度不平衡的数据分布下，FedImp优于所有基线并达到最高准确率。整体而言，FedImp为非IID场景下的联邦学习效率提升提供了有效方案。
原文摘要 · Abstract (English)
Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy. A major challenge in FL is the non-Independent and Identically Distributed (non-IID) nature of data across devices, which hinders training efficiency and slows convergence. To tackle this, we propose Federated Impurity Weighting (FedImp), a novel algorithm that quantifies each device contribution based on the informational content of its local data. These contributions are normalized to compute distinct aggregation weights for the global model update. Extensive experiments on EMNIST and CIFAR-10 datasets show that FedImp significantly improves convergence speed, reducing communication rounds by up to 64.4%, 27.8%, and 66.7% on EMNIST, and 44.2%, 44%, and 25.6% on CIFAR-10 compared to FedAvg, FedProx, and FedAdp, respectively. Under highly imbalanced data distributions, FedImp outperforms all baselines and achieves the highest accuracy. Overall, FedImp offers an effective solution to enhance FL efficiency in non-IID settings.
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