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Found 5 papers, 1 papers with code
MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation
It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy.
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios.
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
In this study, we propose an FL method called Federated Intermediate Layers Learning (FedIN), supporting heterogeneous models without relying on any public dataset.
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning.
Exploiting Features and Logits in Heterogeneous Federated Learning
Felo averages the mid-level features and logits from the clients at the server based on their class labels to provide the average features and logits, which are utilized for further training the client models.
