4 papers
Elastic Mixture of Rank-Wise Experts for Knowledge Reuse in Federated Fine-Tuning
Yebo Wu, Jingguang Li, Zhijiang Guo +1
Federated fine-tuning offers a promising solution for adapting Large Language Models (LLMs) to downstream tasks while safeguarding data privacy. However, its high computational and…
Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
Yebo Wu, Jingguang Li, Chunlin Tian +2
Federated fine-tuning enables privacy-preserving Large Language Model (LLM) adaptation, but its high memory cost limits participation from resource-constrained devices. We propose…
Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
Yebo Wu, Jingguang Li, Zhijiang Guo +1
Federated fine-tuning enables Large Language Models (LLMs) to adapt to downstream tasks while preserving data privacy, but its resource-intensive nature limits deployment on edge d…
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks
Shichen Zhan, Yebo Wu, Chunlin Tian +2
Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumpt…