Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. However, an inadequate client sampling scheme can lead to the selection of unrepresentative subsets, resulting in significant variance in model updates and slowed convergence. Existing sampling methods are either biased or can be further optimized for faster convergence.In this paper, we present DELTA, an unbiased sampling scheme designed to alleviate these issues. DELTA characterizes the effects of client diversity and local variance, and samples representative clients with valuable information for global model updates. In addition, DELTA is a proven optimal unbiased sampling scheme that minimizes variance caused by partial client participation and outperforms other unbiased sampling schemes in terms of convergence. Furthermore, to address full-client gradient dependence,we provide a practical version of DELTA depending on the available clients' information, and also analyze its convergence. Our results are validated through experiments on both synthetic and real-world datasets.

翻译：部分客户端参与已被广泛用于联邦学习（FL）中，以有效减轻通信负担。然而，不合理的客户端采样方案可能导致选择缺乏代表性的子集，从而使模型更新产生显著方差，并减慢收敛速度。现有采样方法存在偏差，或在加速收敛方面仍有优化空间。本文提出DELTA，一种旨在缓解上述问题的无偏采样方案。DELTA刻画了客户端多样性与局部方差的影响，并采样具有价值信息的代表性客户端以进行全局模型更新。此外，DELTA被证明是一种最优的无偏采样方案，能最小化部分客户端参与引起的方差，并在收敛性能上优于其他无偏采样方案。同时，为解决全客户端梯度依赖问题，我们基于可用客户端信息提供了DELTA的实用版本，并分析了其收敛性。通过在合成数据集和真实数据集上的实验，验证了我们的结果。