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Found 6 papers, 1 papers with code
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models.
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
This is evident across tasks in both natural language processing and computer vision domains.
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training.
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
To address these challenges, we propose Resource-aware Federated Learning (RaFL).
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
This method allows efficient multi-model knowledge fusion and the deployment of resource-aware models while preserving model heterogeneity.
RHNAS: Realizable Hardware and Neural Architecture Search
The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges.
