33 citations · 46 across the 13 of their papers we have counts for
4 papers · 1 filter
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
Xiyu Zhao, Qimei Cui, Weicai Li +5
Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide persona…
Two Is Better Than One: Rotations Scale LoRAs
Hongcan Guo, Guoshun Nan, Yuan Yang +9
Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechan…
SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices
Jian Ma, Xinchen Lyu, Jun Jiang +3
Fine-tuning large language models (LLMs) on private, on-device data can empower tailored personalized AI agents. However, fine-tuning LLMs on resource-constrained edge devices face…
Similarity-based Label Inference Attack against Training and Inference of Split Learning
Junlin Liu, Xinchen Lyu, Qimei Cui +1
Split learning is a promising paradigm for privacy-preserving distributed learning. The learning model can be cut into multiple portions to be collaboratively trained at the partic…