Federated Learning (FL) enables decentralized model training across multiple clients while preserving data privacy; however, it faces significant challenges related to statistical heterogeneity and fairness across participating clients. Prior work has shown that dominant clients can disproportionately influence global model updates, leading to performance imbalance, and that manual intervention, such as removing high-performing clients, can partially improve fairness at the cost of automation and scalability. In this study, we propose an adaptive proximal regulation framework that automatically adjusts the proximal regularization term in the federated proximal algorithm to promote fair client participation during training. Unlike fixed regularization approaches, our method dynamically modifies the proximal coefficient based on client performance at each communication round. We evaluate two adaptive strategies under both independent and non-independent data distributions. Experimental results reveal that directly penalizing dominant clients does not consistently improve fairness and may degrade performance in heterogeneous environments. Motivated by this finding, we introduce a reverse adaptive strategy that applies stronger regularization to low-performing clients to reduce excessive local bias. This approach improves early-stage fairness and maintains competitive global accuracy. Our results highlight that effective fairness control in FL requires careful treatment of straggler clients rather than simple suppression of dominant ones. The proposed framework provides a step toward fully automated, fairness-aware federated optimization in real-world heterogeneous settings.