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näkemys - Gradient Harmonization in Federated Learning
Gradient Harmonization: Addressing Non-IID Issue in Federated Learning
Non-IID issue in federated learning is addressed through Gradient Harmonization, improving performance across diverse scenarios.
Addressing Non-IID Issue in Federated Learning with Gradient Harmonization
The author addresses the non-IID issue in federated learning by proposing FedGH, a method that mitigates local drifts through Gradient Harmonization. FedGH consistently enhances FL baselines across diverse benchmarks and scenarios with stronger heterogeneity.
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