6 papers
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
Yusaku Negoya, Feifei Cui, Zilong Zhang +3
Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Priva…
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…
Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning
Xingke Yang, Liang Li, Sicong Li +6
Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a…
PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive 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…
MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning
Liang Li, Xingke Yang, Wen Wu +5
Large Language Model (LLM) at mobile devices and its potential applications never fail to fascinate. However, on-device LLM fine-tuning poses great challenges due to extremely high…
THOR: A Generic Energy Estimation Approach for On-Device Training
Jiaru Zhang, Zesong Wang, Hao Wang +8
Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and…