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Found 5 papers, 0 papers with code
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication.
Learning low-dimensional dynamics from whole-brain data improves task capture
We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data.
Single-Shot Pruning for Offline Reinforcement Learning
We leverage a fixed dataset to prune neural networks before the start of RL training.
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
