FedPrism: 在非独立同分布数据下适应性个性化联邦学习
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
AI总结：
FedPrism通过Prism分解和双流设计，在非独立同分布数据下实现适应性个性化联邦学习，提升模型准确率与泛化能力。
摘要：
联邦学习（FL）在现实部署中面临显著性能下降，其特征是统计异质性（非独立同分布客户端数据）中等至极端。虽然全局聚合策略促进广泛泛化，但往往无法捕捉本地数据分布的多样性，导致个性化效果不佳。我们通过FedPrism框架解决这一问题，该框架采用两种主要策略。首先，它使用Prism分解方法，将每个客户端的模型分为三部分：全局基础、相似客户端的共享组部分和针对独特本地数据的私有部分。这使系统能够自动将相似用户分组，并在数据变化时进行适应。其次，我们引入双流设计，同时运行通用模型和本地专家模型。系统根据专家模型的置信度将预测路由到通用模型和本地专家模型之间。通过系统性的非独立同分布数据分区实验，我们证明FedPrism在非独立同分布数据分区上超越了静态聚合和硬聚类基线，实现了高异质性下的显著准确率提升。这些结果确立了FedPrism作为异构环境联邦学习稳健且灵活的解决方案，有效平衡了可泛化知识与适应性个性化。
英文摘要：
Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data). While global aggregation strategies promote broad generalization, they often fail to capture the diversity of local data distributions, leading to suboptimal personalization. We address this problem with FedPrism, a framework that uses two main strategies. First, it uses a Prism Decomposition method that builds each client's model from three parts: a global foundation, a shared group part for similar clients, and a private part for unique local data. This allows the system to group similar users together automatically and adapt if their data changes. Second, we include a Dual-Stream design that runs a general model alongside a local specialist. The system routes predictions between the general model and the local specialist based on the specialist's confidence. Through systematic experiments on non-IID data partitions, we demonstrate that FedPrism exceeds static aggregation and hard-clustering baselines, achieving significant accuracy gains under high heterogeneity. These results establish FedPrism as a robust and flexible solution for federated learning in heterogeneous environments, effectively balancing generalizable knowledge with adaptive personalization.
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