Model-Heterogeneous Prototypical Federated Learning Over the Air

Over-the-air federated learning (OTA FL) provides a joint computation and communication approach to design FL systems with improved efficiency. By leveraging the superposition property of wireless channels, OTA FL enables the automatic aggregation of intermediate parameters-such as gradients-across a large number of clients, significantly reducing communication overhead while concurrently enhancing transmission privacy. However, gradient aggregation requires all clients to use identical model architectures, a condition often impractical in real-world scenarios due to variations in client hardware and computational capabilities. This mismatch can lead to scalability issues and system incompatibilities. To address this challenge, we propose a model-agnostic method based on model prototypes that enables collaborative training across clients with heterogeneous models. The proposed method bypasses the requirements of the conventional model weight/gradient updates with prototype vector aggregation, without requiring the model structures of all clients to be identical. To the best of our knowledge, the proposed method is the first to explore the prototypical model-heterogeneous OTA FL with desirable training performance and extremely low communication cost. We conducted extensive experiments to verify the efficacy of the proposed method. The results show that our approach not only significantly reduces communication overhead but also exploits the superior capabilities of large models to enhance the performance of smaller models.