<p>The rapidly expanding domain of federated learning, characterized by decentralized training across multiple clients, often grapples with challenges tied to client reliability and bandwidth constraints. This paper introduces the Weighted Federated Communication (Weighted FedCOM) paradigm-an innovative enhancement over traditional federated learning approaches. At its core, Weighted FedCOM seamlessly integrates the robustness of weighted aggregation with the efficiency gains from model compression. By assigning weights to client contributions based on the accuracy of their local models, our approach ensures that more reliable models exert a greater influence on the global aggregated model. Concurrently, the incorporation of model compression techniques offers substantial reductions in communication overhead, a typical bottleneck in federated learning. Preliminary evaluations demonstrate that Weighted FedCOM significantly outperforms conventional federated learning methodologies in terms of convergence speed and global model accuracy. This research illuminates a promising avenue for bolstering the efficacy and efficiency of federated learning in diverse, decentralized systems.</p>