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Found 5 papers, 4 papers with code
Exploiting Label Skews in Federated Learning with Model Concatenation
However, non-IID data has been a key challenge in FL, which could significantly degrade the accuracy of the final model.
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Thus, a natural question is how those open environment challenges look like and how existing incremental learning algorithms perform on real-world relational data streams.
Deep Predictive Learning of Carotid Stenosis Severity
Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head.
Federated Learning on Non-IID Data Silos: An Experimental Study
We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases.
BiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks
Inside each stage, BiHand adopts a novel bisecting design which allows the networks to encapsulate two closely related information (e. g. 2D keypoints and silhouette in 2D seeding stage, 3D joints, and depth map in 3D lifting stage, joint rotations and shape parameters in the mesh generation stage) in a single forward pass.
