表示对齐的多尺度个性化联邦学习
Representation-Aligned Multi-Scale Personalization for Federated Learning
AI总结：
本文提出FRAMP框架，通过生成客户端特定模型实现个性化和资源适应的联邦学习，提升泛化能力和适应性。
摘要：
在联邦学习（FL）中，满足具有不同资源约束的客户端仍是一个重大挑战。广泛采用的方法是使用共享的全尺寸模型，每个客户端从中提取与其计算预算对齐的子模型。然而，无论具体评分策略如何，这些方法都依赖于相同的全局骨干网络，限制了客户端之间的结构多样性和表示适应性。本文提出了FRAMP，一个统一的个性化和资源适应的联邦学习框架。不同于依赖固定全局模型，FRAMP从紧凑的客户端描述符生成客户端特定模型，实现对数据特性和计算预算的细粒度适应。每个客户端训练一个定制的轻量级子模型，并将其学习的表示与他人对齐以保持全局语义一致性。在视觉和图基准测试中，广泛实验表明FRAMP在广泛的客户端设置中增强了泛化能力和适应性。
英文摘要：
In federated learning (FL), accommodating clients with diverse resource constraints remains a significant challenge. A widely adopted approach is to use a shared full-size model, from which each client extracts a submodel aligned with its computational budget. However, regardless of the specific scoring strategy, these methods rely on the same global backbone, limiting both structural diversity and representational adaptation across clients. This paper presents FRAMP, a unified framework for personalized and resource-adaptive federated learning. Instead of relying on a fixed global model, FRAMP generates client-specific models from compact client descriptors, enabling fine-grained adaptation to both data characteristics and computational budgets. Each client trains a tailored lightweight submodel and aligns its learned representation with others to maintain global semantic consistency. Extensive experiments on vision and graph benchmarks demonstrate that FRAMP enhances generalization and adaptivity across a wide range of client settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
