<ul><li>Federated Learning (FL) is a popular solution for privacy-sensitive applications that allows collaborative training while ensuring data privacy across distributed edge devices.</li><li>FL faces challenges due to statistical heterogeneity, particularly domain heterogeneity, which hinders the convergence of the global model.</li><li>A new framework called FedAPC (Federated Augmented Prototype Contrastive Learning) has been introduced to address the challenge by improving the generalization ability of the FL global model under domain heterogeneity using prototype augmentation.</li><li>Experimental results on the Office-10 and Digits datasets show that FedAPC outperforms state-of-the-art baselines, demonstrating superior performance in enhancing feature diversity and model robustness.</li></ul>

Robust Federated Learning on Edge Devices with Domain Heterogeneity

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