4 papers
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL)…
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the…
ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated le…
Distributed Precoding for Cell-free Massive MIMO in O-RAN: A Multi-agent Deep Reinforcement Learning Framework
Mohammad Hossein Shokouhi, Vincent W. S. Wong
Cell-free massive multiple-input multiple-output (MIMO) is a key technology for next-generation wireless systems. The integration of cell-free massive MIMO within the open radio ac…