发表机构
Centre for the Science of Learning & Technology (SLATE), University of Bergen（卑尔根大学学习与技术科学中心（SLATE））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦学习均匀压缩浪费带宽的问题，提出逐层预算化自适应传输（LBAT），将通信视为资源分配，动态估计层价值并用动态规划优化秩和位配置，在极端预算下优于现有基线。
AI 中文摘要
联邦学习在客户端上传高维模型更新时面临严重的通信瓶颈。现有方法通常跨所有层均匀压缩这些更新。这种均匀方法忽略了不同参数块的异构价值，并在不敏感层上浪费有限的带宽。为解决此问题，我们提出了逐层预算化自适应传输（LBAT）。LBAT将极端上行链路预算下的联邦通信重新定义为资源分配问题。我们的框架利用本地训练信号动态估计不同层的传输价值。然后，它采用精确的字节动态规划分配器，在严格预算下确定最优秩和位配置。我们在高度异构的联邦表格预测和数据生成任务上验证了LBAT。大量实验表明，在各种极端预算机制下，LBAT始终优于均匀秩、均匀量化和固定压缩基线。此外，它在保持基本分布保真度的同时，实现了显著更好的通信和效用权衡。
英文摘要
Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these updates uniformly across all layers. This uniform approach ignores the heterogeneous value of different parameter blocks and wastes limited bandwidth on insensitive layers. To address this issue, we propose Layer-wise Budgeted Adaptive Transmission (LBAT). LBAT reframes federated communication under extreme uplink budgets as a resource allocation problem. Our framework dynamically estimates the transmission value of different layers utilising local training signals. It then employs an exact byte dynamic programming allocator to determine optimal rank and bit configurations under strict budgets. We validate LBAT on highly heterogeneous federated tabular prediction and data generation tasks. Extensive experiments demonstrate that LBAT consistently outperforms uniform rank, uniform quantisation, and fixed compression baselines across various extreme budget regimes. Furthermore, it achieves significantly better communication and utility tradeoffs while preserving essential distributional fidelity.
