REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings
B Radovič, V Pejović - arXiv preprint arXiv:2309.14088, 2023 - arxiv.org
Clustering clients into groups that exhibit relatively homogeneous data distributions
represents one of the major means of improving the performance of federated learning (FL) …
represents one of the major means of improving the performance of federated learning (FL) …
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. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Though successful, federated learning (FL) presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
Federated learning with server learning: Enhancing performance for non-iid data
Federated Learning (FL) has emerged as a means of distributed learning using local data
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
Flis: Clustered federated learning via inference similarity for non-iid data distribution
M Morafah, S Vahidian, W Wang… - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
Conventional federated learning (FL) approaches are ineffective in scenarios where clients
have significant differences in the distributions of their local data. The Non-IID data …
have significant differences in the distributions of their local data. The Non-IID data …
A Bayesian Framework for Clustered Federated Learning
One of the main challenges of federated learning (FL) is handling non-independent and
identically distributed (non-IID) client data, which may occur in practice due to unbalanced …
identically distributed (non-IID) client data, which may occur in practice due to unbalanced …
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Federated learning (FL) is a well established method for performing machine learning tasks
over massively distributed data. However in settings where data is distributed in a non-iid …
over massively distributed data. However in settings where data is distributed in a non-iid …
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Federated Learning (FL) has emerged as a pivotal framework for the development of
effective global models (global FL) or personalized models (personalized FL) across clients …
effective global models (global FL) or personalized models (personalized FL) across clients …
A Study of Enhancing Federated Learning on Non-IID Data with Server Learning
Federated Learning (FL) has emerged as a means of distributed learning using local data
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …
Multi-initial-center federated learning with data distribution similarity-aware constraint
Federated Learning (FL) has recently attracted high attention since it allows clients to
collaboratively train a model while the training data remains local. However, due to the …
collaboratively train a model while the training data remains local. However, due to the …