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Found 8 papers, 5 papers with code
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Specifically, we propose a systematical algorithm termed Extended Single-Step Synthetic Features Compressing (E-3SFC), which consists of three sub-components, i. e., the Single-Step Synthetic Features Compressor (3SFC), a double-way compression algorithm, and a communication budget scheduler.
Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training
Re-training the token-reduced model enhances the performance of Mamba, by effectively rebuilding the key knowledge.
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
In this paper, we propose a new PFL framework called FedPFT to address the mismatch problem while enhancing the quality of the feature extractor.
A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training
Training diffusion models is always a computation-intensive task.
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data.
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning.
Personalized Federated Learning with Hidden Information on Personalized Prior
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global model training without direct data sharing.
Ranked #1 on
Image Classification
on Fashion-MNIST
(Accuracy metric)
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Specifically, the performance of each worker is evaluatedfirst based on the fact in the previous epoch, and then the batch size and datasetpartition are dynamically adjusted in consideration of the current performanceof the worker, thereby improving the utilization of the cluster.
