通过最大化标签熵选择客户端，提升联邦学习在数据不均衡下的准确率。
Optimizing Federated Learning by Entropy-Based Client Selection
- 按标签分布熵值筛选客户端，确保全局模型覆盖所有类别。
- 在标准设置下准确率最高提升6%，低参与率下提升超30%。
- 兼容现有算法，适合隐私敏感且数据分布不均的场景。
尽管深度学习在自然语言处理和计算机视觉等领域取得了突破，但其对集中式数据集的依赖引发了严重的隐私问题。联邦学习通过允许多个客户端在不泄露数据隐私的前提下协同训练全局深度学习模型，解决了这一问题。然而，在标签偏斜（即客户端间标签分布不一致）的情况下，模型性能会下降。为此，本文提出一种新方法 FedEntOpt：每轮训练中，通过最大化聚合标签分布的熵来选择客户端，确保全局模型能接触到所有可用类别的数据。在多个基准数据集上的大量实验表明，该方法在标准设置下相比多种先进算法，分类准确率最高提升6%，无论模型大小；在低参与率和客户端掉线场景下，准确率提升超过30%。此外，FedEntOpt可与现有算法结合，使其准确率提升超40%。重要的是，即使在应用差分隐私的情况下，其性能仍保持稳定。
原文摘要 · Abstract (English)
Although deep learning has revolutionized domains such as natural language processing and computer vision, its dependence on centralized datasets raises serious privacy concerns. Federated learning addresses this issue by enabling multiple clients to collaboratively train a global deep learning model without compromising their data privacy. However, the performance of such a model degrades under label skew, where the label distribution differs between clients. To overcome this issue, a novel method called FedEntOpt is proposed. In each round, it selects clients to maximize the entropy of the aggregated label distribution, ensuring that the global model is exposed to data from all available classes. Extensive experiments on multiple benchmark datasets show that the proposed method outperforms several state-of-the-art algorithms by up to 6% in classification accuracy under standard settings regardless of the model size, while achieving gains of over 30% in scenarios with low participation rates and client dropout. In addition, FedEntOpt offers the flexibility to be combined with existing algorithms, enhancing their classification accuracy by more than 40%. Importantly, its performance remains unaffected even when differential privacy is applied.
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