Progress-Guided Client Participation in Federated Learning

Communication is a primary bottleneck in federated learning (FL), yet standard pipelines typically fix the number of participating clients per round. A static participation budget may incur unnecessary communication when additional participation offers limited optimization benefit and, conversely, fail to capture sufficient client diversity under strong data heterogeneity. We propose Intelligent Selection of Participants (ISP), an adaptive mechanism that treats the per-round client count as a controllable optimization variable and adjusts it online to balance communication and optimization progress. ISP uses a lightweight client-count search and complements methods that select which clients participate. Across public vision and NLP benchmarks and a large-scale real-world ECG dataset, ISP reduces overall communication by up to 30% while maintaining the downstream performance of fixed-budget baselines. To assess generality, we evaluate ISP across a wide range of heterogeneity levels and system settings, demonstrating robust adaptation under noisy per-round estimates. We also provide a convergence analysis of the adaptive client-count rule underlying ISP.