  The standard client selection algorithms for Federated Learning (FL) are
often unbiased and involve uniform random sampling of clients. This has been
proven sub-optimal for fast convergence under practical settings characterized
by significant heterogeneity in data distribution, computing, and communication
resources across clients. For applications having timing constraints due to
limited communication opportunities with the parameter server (PS), the client
selection strategy is critical to complete model training within the fixed
budget of communication rounds. To address this, we develop a biased client
selection strategy, GreedyFed, that identifies and greedily selects the most
contributing clients in each communication round. This method builds on a fast
approximation algorithm for the Shapley Value at the PS, making the computation
tractable for real-world applications with many clients. Compared to various
client selection strategies on several real-world datasets, GreedyFed
demonstrates fast and stable convergence with high accuracy under timing
constraints and when imposing a higher degree of heterogeneity in data
distribution, systems constraints, and privacy requirements.
