Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While FL preserves data privacy by eliminating centralized storage, it encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model due to the heterogeneity in client data distributions. Among the various forms of data heterogeneity, label skew emerges as a particularly formidable and prevalent issue, especially in domains such as image classification. To address these challenges, we begin with comprehensive experiments to pinpoint the underlying issues in the FL training process. Based on our findings, we then introduce an innovative dual-strategy approach designed to effectively resolve these issues. First, we introduce an adaptive loss function for client-side training, meticulously crafted to preserve previously acquired knowledge while maintaining an optimal equilibrium between local optimization and global model coherence. Secondly, we develop a dynamic aggregation strategy for aggregating client models at the server. This approach adapts to each client's unique learning patterns, effectively addressing the challenges of diverse data across the network. Our comprehensive evaluation, conducted across three diverse real-world datasets, coupled with theoretical convergence guarantees, demonstrates the superior efficacy of our method compared to several established state-of-the-art approaches.

翻译：联邦学习（FL）通过整合来自不同客户端的本地优化模型形成统一的全局模型，标志着分布式模型训练的一种变革性方法。尽管FL通过消除集中式存储保护了数据隐私，但由于客户端数据分布的异质性，它面临着性能下降、收敛速度减慢以及全局模型鲁棒性降低等重大挑战。在多种形式的数据异质性中，标签偏斜成为一个尤为严峻且普遍的问题，特别是在图像分类等领域。为应对这些挑战，我们首先通过全面的实验来定位FL训练过程中的根本问题。基于我们的发现，我们随后引入了一种创新的双重策略方法，旨在有效解决这些问题。首先，我们为客户端训练引入了一种自适应损失函数，该函数经过精心设计，既能保留先前获得的知识，又能在本地优化与全局模型一致性之间保持最佳平衡。其次，我们为服务器端聚合客户端模型开发了一种动态聚合策略。该方法能适应每个客户端独特的学习模式，有效应对网络中多样化数据带来的挑战。我们在三个不同的真实世界数据集上进行的全面评估，结合理论收敛性保证，证明了我们的方法相较于多种现有先进方法具有更优的效能。