用信息量取代带宽，智能分配联邦学习的通信资源。
HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning
- 以客户端信息量为依据决定选谁、压多少、怎么加权。
- 在CIFAR-10上提速1.78倍，流量减少18.2%。
- 适合数据异构严重或带宽与信息量冲突的场景。
联邦学习系统通常按链路速度分配梯度压缩，但在非独立同分布（non-IID）数据下，带宽与数据信息量常不匹配甚至相反。此时仅依赖带宽会导致最有信息量的梯度被过度压缩。为此提出HeteRo-Select框架，以客户端信息量得分作为压缩决策主驱动，联合控制每轮的客户端选择、压缩比和服务器加权，带宽仅作硬上限。信息量比例选择可有效降低所选子集的有效异质性；信息量比例压缩可在固定流量下降低整体top-$k$误差。在标准FedCG仿真下，该方法在CIFAR-10上实现1.78倍加速和18.2%流量减少。相同配置从7,850参数逻辑回归扩展至11.27M参数ResNet-18，在四个基准中的三个达到目标准确率。当带宽与信息量故意反相关时，仍以更少流量达成目标精度。
原文摘要 · Abstract (English)
Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks compressing the most informative gradients hardest. We propose HeteRo-Select, a framework that replaces bandwidth with a per-client informativeness score as the primary driver of compression. The score jointly governs three decisions per round: client selection, compression ratio, and server aggregation weight, with bandwidth retained only as a hard ceiling. Score-proportional selection provably reduces the effective heterogeneity of the chosen subset; score-proportional compression provably lowers aggregate top-$k$ error at fixed traffic. Under the exact FedCG simulation protocol, HeteRo-Select delivers a $1.78\times$ speedup and an $18.2\%$ reduction in traffic on CIFAR-10. The same configuration, unchanged, scales from a $7{,}850$-parameter logistic regression to an $11.27$M-parameter ResNet-18, hitting the accuracy target on three of four benchmarks. When bandwidth and informativeness are deliberately anti-correlated, the method still achieves the target accuracy with less traffic than the normal-bandwidth run.
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