What are the applications of curriculum learning in federated learning?
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Curriculum learning in federated learning has various applications. It helps alleviate data heterogeneity among clients , enhances generalization performance by actively scheduling user learning pace with a dynamic multi-phase curriculum ^[Context_3 , and controls the order of training samples to improve classification performance in multi-site breast cancer classification ^[Context_1 . By implementing curriculum learning principles such as curriculum, anti-curriculum, and random curriculum, federated learning can effectively address challenges related to non-IID data distributions, improve convergence rates, and optimize model training across multiple institutions. Additionally, the use of memory-aware curriculum learning methods can further enhance the effectiveness of federated adversarial learning for breast cancer classification tasks.
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| Papers (5) | Insight |
|---|---|
| Curriculum learning in federated learning helps alleviate data heterogeneity, especially in non-IID scenarios, by making the objective landscape less convex, aiding faster convergence and improving overall model performance. | |
1 Citations | Applications of curriculum learning in federated learning include synchronously correlating user heterogeneity, forming a global curriculum, and improving generalization performance by aligning learning pace dynamically across multiple phases. |
14 Nov 2022 | Curriculum learning in federated learning aids in handling user heterogeneity by synchronously scheduling learning pace, improving generalization performance, and aligning global and local objectives. |
Open access•Posted Content | Memory-aware curriculum learning in federated settings controls the order of training samples, focusing on forgotten ones post-global model deployment, enhancing breast cancer classification performance across multiple institutions. |
01 Feb 2023 | Memory-aware curriculum learning in federated settings controls sample order, focusing on forgotten data, enhancing breast cancer classification across multiple institutions while respecting privacy and addressing domain shift. |
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