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Heterogeneous lora for federated fine-tuning of on-device foundation models
YJ Cho, L Liu, Z Xu, A Fahrezi, G Joshi - arxiv preprint arxiv:2401.06432, 2024 - arxiv.org
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and
federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs …
federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs …
Towards super compressed neural networks for object identification: Quantized low-rank tensor decomposition with self-attention
Deep convolutional neural networks have a large number of parameters and require a
significant number of floating-point operations during computation, which limits their …
significant number of floating-point operations during computation, which limits their …
Efficient compression of overparameterized deep models through low-dimensional learning dynamics
Overparameterized models have proven to be powerful tools for solving various machine
learning tasks. However, overparameterization often leads to a substantial increase in …
learning tasks. However, overparameterization often leads to a substantial increase in …