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We propose a novel paradigm in federated learning where a powerful server model learns from multiple teacher clients and transfers knowledge back to boost client performance, addressing the limitations of model complexity on edge devices. Our framework achieves superior performance on image classification tasks, providing flexibility for heterogeneous client architectures, robustness against attacks, and efficient communication between clients and the server.