面向并行分割学习与全局采样的延迟感知客户端分配
Latency-Aware Client Assignment for Parallel Split Learning With Global Sampling
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中文总结 AI 辅助
提出延迟预算并行分割学习，通过流或贪心变体分配客户端，在保持类别目标的同时减少建模训练时间，并在CIFAR-10和Tiny ImageNet上验证了有效性。
中文摘要 AI 辅助
在跨孤岛分割学习中，当不同客户端的类别分布存在差异时，并行分割学习与全局采样会形成具有代表性的池化批次，但在多个客户端可以提供相同类别时，该方法忽略了客户端延迟。我们提出了具有全局采样的延迟预算并行分割学习，该方法将每个池化批次的整数类别目标与提供其样本的客户端选择分离开来。流变体将该分配问题表述为整数网络流问题，并最小化当前目标的建模客户端侧完成时间。快速变体使用贪心下一完成规则来降低调度构建成本。两种变体都保持目标流，并且每个本地样本在每个周期内仅使用一次。规划规则在考虑构建两个候选调度的成本的同时，在变体之间进行选择。在CIFAR-10上，流变体将建模训练时间减少了6.75%，最终准确率下降了0.30个百分点。在Tiny ImageNet上，每个客户端有20个候选类别，快速变体将建模时间减少了16.87%，并且比延迟无关基线更早达到所有四个验证目标。在405次调度比较中，规划规则在96.54%的情况下保持在较低实现成本的2%以内。在我们的评估中，延迟感知提供者分配在不改变规定类别目标的情况下减少了建模训练时间，而首选变体取决于分配节省是否超过调度构建开销。
英文摘要
In cross-silo split learning, Parallel Split Learning with Global Sampling forms representative pooled batches when class distributions differ across clients, but ignores client delay when several clients can supply the same class. We introduce Latency Budgeted Parallel Split Learning with Global Sampling, which separates each pooled batch's integer class target from the choice of clients that supply its examples. The flow variant formulates this assignment as an integral network-flow problem and minimizes modeled client-side completion time for the current target. The fast variant uses a greedy next-completion rule to reduce schedule-construction cost. Both preserve the target stream and use every local example once per epoch. A planning rule selects between the variants while accounting for the cost of constructing both candidate schedules. On CIFAR-10, the flow variant reduces modeled training time by 6.75%, with a 0.30 percentage-point decrease in final accuracy. On Tiny ImageNet with 20 candidate classes per client, the fast variant reduces modeled time by 16.87% and reaches all four validation targets earlier than the latency-unaware baseline. Across 405 schedule comparisons, the planning rule stays within 2% of the lower realized cost in 96.54% of cases. In our evaluation, latency-aware provider assignment reduces modeled training time without changing the prescribed class targets, while the preferred variant depends on whether assignment savings outweigh schedule-construction overhead.
