提出新方法缓解异构联邦学习中的模型偏差，提升新客户端适配能力。
FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
- 通过参数引导的全局聚合与局部去偏分类协同优化
- 在多领域数据集上显著优于现有基线方法
- 适合需快速适配新客户端的异构联邦学习场景
异构联邦学习（HFL）旨在实现不同实体间高效且隐私保护的合作。由于新加入客户端需大量调整和额外训练以适应现有系统，如何在异构数据下将联邦学习模型泛化至未见客户端的问题日益关键。因此，我们指出联邦域泛化中的两个未解难题：优化发散与性能发散。为此，我们提出FedRD，一种新型异构感知联邦学习算法，通过协同利用参数引导的全局泛化聚合与局部去偏分类，减少发散，旨在为参与客户端及未见客户端获得最优全局模型。在多个公开多领域数据集上的大量实验表明，该方法在解决此问题上显著优于竞争基线。
原文摘要 · Abstract (English)
Heterogeneous federated learning (HFL) aims to ensure effective and privacy-preserving collaboration among different entities. As newly joined clients require significant adjustments and additional training to align with the existing system, the problem of generalizing federated learning models to unseen clients under heterogeneous data has become progressively crucial. Consequently, we highlight two unsolved challenging issues in federated domain generalization: Optimization Divergence and Performance Divergence. To tackle the above challenges, we propose FedRD, a novel heterogeneity-aware federated learning algorithm that collaboratively utilizes parameter-guided global generalization aggregation and local debiased classification to reduce divergences, aiming to obtain an optimal global model for participating and unseen clients. Extensive experiments on public multi-domain datasets demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.
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