FedBudget: A Budget-Aware Federated Learning Method for Communication-Constrained Distributed Data Mining

Abstract

Federated learning enables collaborative data mining without centralizing raw data, but communication budgets remain a practical bottleneck in distributed deployment. Existing federated optimization methods mainly address statistical heterogeneity or aggregation stability, while client participation is often treated as full participation or random sampling. This paper proposes FedBudget, a budget-aware client selection method for communication-constrained federated data mining. In each round, FedBudget constructs a scheduling score from historical utility, stability, freshness, communication cost, and a coverage-aware penalty, and then greedily selects clients under a given communication budget. The aggregation stage follows the standard sample-size-weighted selected-client FedAvg rule, which makes the scheduling contribution directly attributable. Experiments on AI4I, Mammography, Shuttle, SMD, and SWaT compare FedBudget with representative federated optimization and scheduling baselines. Statistical analysis shows that FedBudget significantly reduces communication cost and improves communication-normalized performance relative to budgeted optimization baselines, while maintaining competitive AUC and PR-AUC. Larger-scale experiments with 20 and 50 simulated clients show mean performance-per-MB improvements of 4.019 and 1.945, respectively, together with lower mean communication cost. Sensitivity and convergence analyses confirm the robustness of the proposed scheduling mechanism and its stable communication–performance trade-off. These results indicate that explicit budget-aware participation modeling improves communication efficiency in federated data mining while preserving a simple and compatible training pipeline.