AI 中文总结
针对异构边缘设备联邦学习中的资源与域偏移问题，提出FedSAP框架，通过结构化剪枝实现三态通道分配，隔离域敏感更新，在Digits和Office-Caltech上超越基线并支持高剪枝率。
AI 中文摘要
异构边缘设备上的联邦学习（FL）必须同时适应不平衡的资源预算和域偏移的本地数据。现有的资源自适应方法决定每个客户端训练模型的多少，但不决定保留容量应位于何处或应如何共享，而联邦域泛化方法通常假设共享完整架构。因此，统一压缩可能丢弃高效用通道，单一聚合路径可能混合可迁移特征与域敏感更新。我们提出FedSAP，一种域感知的异构联邦学习框架，将结构化剪枝视为受预算约束的三态通道分配。FedSAP将每个保留比率转换为非均匀层预算，将稳定通道分配给全局池，将有用的域敏感通道分配给伪域特定的私有池，并将低效用通道分配给丢弃状态。这种分区使广泛有用的特征受益于跨客户端池化，同时将域敏感更新与不兼容客户端隔离。域引导分配从浅梯度相似性推断伪域，而类型匹配聚合将每个通道限制在其预期的共享范围内。在三个随机种子下，FedSAP在Digits和Office-Caltech上达到76.00%和72.67%的平均全局准确率，超过最强基线1.70和4.92个百分点，同时支持异构客户端高达80%的客户端剪枝比率。
英文摘要
Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource-adaptive methods decide how much of a model each client trains but not where retained capacity should reside or how it should be shared, whereas federated domain-generalization methods usually assume a shared full architecture. Uniform compression can therefore discard high-utility channels, and a single aggregation path can mix transferable features with domain-sensitive updates. We propose FedSAP, a domain-aware heterogeneous FL framework that casts structured pruning as budget-constrained tri-state channel allocation. FedSAP converts each keep ratio into non-uniform layer budgets, assigns stable channels to a Global pool, useful domain-sensitive channels to pseudo-domain-specific Private pools, and low-utility channels to a Dropped state. This partition lets broadly useful features benefit from cross-client pooling while isolating domain-sensitive updates from incompatible clients. Domain-Guided Assignment infers pseudo-domains from shallow-gradient similarity, while Type-Matched Aggregation restricts each channel to its intended sharing scope. Across three random seeds, FedSAP reaches 76.00% and 72.67% mean global accuracy on Digits and Office-Caltech, exceeding the strongest baseline by 1.70 and 4.92 percentage points while supporting client pruning ratios of up to 80% across heterogeneous clients.
