Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without centralizing client data. However, most existing methods rely on single-objective optimization and heuristic aggregation strategies that neglect client-specific characteristics, resulting in performance degradation, unfair model behavior, and inefficient convergence under heterogeneous client settings. In this work, we propose FedMOAR, a Multi-Objective Adaptive Regularization strategy that jointly optimizes global model accuracy, client-level fairness, and communication efficiency. Unlike conventional FL approaches that apply uniform regularization or focus solely on minimizing global loss, FedMOAR dynamically adjusts its regularization coefficients based on model divergence, fairness penalties, and accuracy compensation. We evaluate FedMOAR on MNIST and NSL-KDD datasets using Dirichlet-based heterogeneous (non-IID) data partitions ( \(\alpha \) = 0.1, 0.5, 1.0) that induce variability in data volume and class distributions under both partial and full client participation. Experimental results show that FedMOAR consistently outperforms baselines such as FedAvg, FedProx, FairFed, and FedVal. Specifically, it achieves higher F1-scores, lower min-max accuracy gap (MMAG), and improved Jain’s Fairness Index (JFI), while demonstrating up to 2.4 \(\times \) speedup. Even in scenarios with comparable test accuracy, FedMOAR yields significantly better F1 and JFI values, and lower MMAG, confirming its effectiveness as a fair and efficient FL solution. These results highlight FedMOAR’s practical value in real-world deployments characterized by heterogeneous data and client diversity.