2 citations · 2 across the 8 of their papers we have counts for
8 papers
Eco-MobiLLM: Efficient LLM Fine-Tuning on the Mobile Device via Server Assisted Side-Tuning
Xingke Yang, Liang Li, Zhiyi Wan +6
There is a huge gap between numerous intriguing applications fostered by on-device large language model (LLM) fine-tuning (FT) from fresh mobile data and the limited resources of a…
Do Protein Transformers Have Biological Intelligence?
Fudong Lin, Wanrou Du, Jinchan Liu +5
Deep neural networks, particularly Transformers, have been widely adopted for predicting the functional properties of proteins. In this work, we focus on exploring whether Protein…
Adaptive Budget Allocation for Orthogonal-Subspace Adapter Tuning in LLMs Continual Learning
Zhiyi Wan, Wanrou Du, Liang Li +2
Large language models (LLMs) often suffer from catastrophic forgetting in continual learning (CL) scenarios, where performance on previously learned tasks degrades severely while t…
Differential Privacy Preserving Distributed Quantum Computing
Hui Zhong, Keyi Ju, Jiachen Shen +5
Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a…
FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
Jiaxiang Geng, Boyu Li, Xiaoqi Qin +4
Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping…
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling
Huai-an Su, Jiaxiang Geng, Liang Li +5
As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating…
Tuning Quantum Computing Privacy through Quantum Error Correction
Hui Zhong, Keyi Ju, Manojna Sistla +4
Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to…
Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
Keyi Ju, Xiaoqi Qin, Hui Zhong +3
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, da…