用自适应约束提升个性化联邦学习性能，尤其适合特征差异大的场景。
Adaptive Latent-Space Constraints in Personalized Federated Learning
- 引入自适应MMD度量，动态调整客户端间分布差异约束
- 在多任务上显著提升模型表现，特征异质性越强效果越明显
- 可通用到其他个性化联邦学习方法，适用性强
联邦学习（FL）是一种在分布式客户端数据上训练深度学习模型的有效方法，同时增强数据安全与隐私保护。由于各客户端数据存在统计异质性，个性化联邦学习（pFL）成为研究热点，旨在融合全局学习与本地建模。本文研究基于理论支持的自适应最大均值差异（MMD）度量在pFL中的有效性，重点针对Ditto这一先进方法。实验表明，该度量能显著提升多种任务的模型性能，尤其在特征异质性显著的场景下优势明显。进一步实验证明，该方法可直接推广至其他pFL框架，并在多个数据集上取得一致改进。结果表明，针对不同异质性类型设计定制化约束是未来优化方向。
原文摘要 · Abstract (English)
Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client's unique characteristics. This work investigates the efficacy of theoretically supported, adaptive MMD measures in pFL, primarily focusing on the Ditto framework, a state-of-the-art technique for distributed data heterogeneity. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. Additional experiments demonstrate that such measures are directly applicable to other pFL techniques and yield similar improvements across a number of datasets. Finally, the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.
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