  Federated learning (FL) faces challenges of intermittent client availability
and computation/communication efficiency. As a result, only a small subset of
clients can participate in FL at a given time. It is important to understand
how partial client participation affects convergence, but most existing works
have either considered idealized participation patterns or obtained results
with non-zero optimality error for generic patterns. In this paper, we provide
a unified convergence analysis for FL with arbitrary client participation. We
first introduce a generalized version of federated averaging (FedAvg) that
amplifies parameter updates at an interval of multiple FL rounds. Then, we
present a novel analysis that captures the effect of client participation in a
single term. By analyzing this term, we obtain convergence upper bounds for a
wide range of participation patterns, including both non-stochastic and
stochastic cases, which match either the lower bound of stochastic gradient
descent (SGD) or the state-of-the-art results in specific settings. We also
discuss various insights, recommendations, and experimental results.
