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Found 6 papers, 2 papers with code
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies.
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy.
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
Through a series of experiments, we have uncovered two critical insights that shed light on the training and parameter inefficiency of LoRA.
Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
In order to preserve the feature representation of each block, we decouple the whole training process into two stages: progressive model shrinking and progressive model growing.
Breaking On-device Training Memory Wall: A Systematic Survey
In this systematic survey, we aim to explore the current state-of-the-art techniques for breaking on-device training memory walls, focusing on methods that can enable larger and more complex models to be trained on resource-constrained devices.
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading
Some DNN models were used in AVSR like Multimodal Deep Autoencoders (MDAEs), Multimodal Deep Belief Network (MDBN) and Multimodal Deep Boltzmann Machine (MDBM) that actually work better than traditional methods.
Audio-Visual Speech Recognition
Automatic Speech Recognition
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