  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.
