Federated Learning
A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm
·2684 words·13 mins·
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Machine Learning
Federated Learning
🏢 Sun Yat-Sen University
FedSAK, a novel federated multi-task learning framework, flexibly handles data, model, and task heterogeneity using tensor trace norm to learn correlations among client models, achieving superior perf…
A Kernel Perspective on Distillation-based Collaborative Learning
·2168 words·11 mins·
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Machine Learning
Federated Learning
🏢 Korea Advanced Institute of Science and Technology
This paper introduces DCL-KR and DCL-NN, novel distillation-based collaborative learning algorithms achieving nearly minimax optimal convergence rates in heterogeneous environments without direct data…
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
·1730 words·9 mins·
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Machine Learning
Federated Learning
🏢 University of Texas at Austin
FedBNN: a novel Bayesian framework for personalized federated learning, achieves superior performance in heterogeneous settings while ensuring strict privacy via differential privacy.
$C^2M^3$: Cycle-Consistent Multi-Model Merging
·3768 words·18 mins·
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Machine Learning
Federated Learning
🏢 Sapienza University of Rome
C2M³: A novel data-free method ensures cycle-consistent merging of neural networks, significantly improving model aggregation across various architectures and datasets.
(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
·2049 words·10 mins·
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AI Generated
Machine Learning
Federated Learning
🏢 KAIST
Federated Semi-Supervised Learning (FSSL) struggles with limited labeled data. (FL)² bridges this gap using adaptive thresholding, sharpness-aware consistency regularization, and learning status-awar…