2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
PHOENIX: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint
Haotian Xie, Junlin Chen, Mingkai Zheng +2
State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware…
FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints
Lishan Yang, Wei Emma Zhang, Nam Kha Nguygen +4
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, p…
FedDPG: An Adaptive Yet Efficient Prompt-tuning Approach in Federated Learning Settings
Ali Shakeri, Wei Emma Zhang, Amin Beheshti +3
Pre-trained Language Models (PLMs) have demonstrated impressive performance in various NLP tasks. However, traditional fine-tuning methods for leveraging PLMs for downstream tasks…
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Lishan Yang, Wei Emma Zhang, Quan Z. Sheng +3
In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources…
The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models
Riccardo Pinciroli, Lishan Yang, Jacob Alter +1
Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of ha…