<ul><li>One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions.</li><li>The paper proposes a novel approach called FedMerge that can create a personalized model per client by merging multiple global models with optimized weights.</li><li>FedMerge allows a few global models to serve many non-IID clients without requiring further local fine-tuning.</li><li>The approach outperforms existing FL methods across different non-IID settings in terms of performance.</li></ul>

FedMerge: Federated Personalization via Model Merging

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