3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…