Search Results for author:
Found 3 papers, 0 papers with code
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation.
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting
To avoid catastrophic forgetting, the main server of KoReA-SFL selects multiple assistant devices for knowledge replay according to the training data distribution of each server-side branch-model portion.
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
