Search Results for author:
Found 5 papers, 5 papers with code
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup.
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants.
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server.
