  Federated learning methods typically learn a model by iteratively sampling
updates from a population of clients. In this work, we explore how the number
of clients sampled at each round (the cohort size) impacts the quality of the
learned model and the training dynamics of federated learning algorithms. Our
work poses three fundamental questions. First, what challenges arise when
trying to scale federated learning to larger cohorts? Second, what parallels
exist between cohort sizes in federated learning and batch sizes in centralized
learning? Last, how can we design federated learning methods that effectively
utilize larger cohort sizes? We give partial answers to these questions based
on extensive empirical evaluation. Our work highlights a number of challenges
stemming from the use of larger cohorts. While some of these (such as
generalization issues and diminishing returns) are analogs of large-batch
training challenges, others (including training failures and fairness concerns)
are unique to federated learning.
