2 citations · 4 across the 6 of their papers we have counts for
6 papers
SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC
Jinglong Luo, Zhuo Zhang, Yehong Zhang +6
Large Language Models (LLMs) have revolutionized numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains such as healthcare and finance remains cons…
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
Jinglong Luo, Guanzhong Chen, Yehong Zhang +6
With the growing deployment of pre-trained models like Transformers on cloud platforms, privacy concerns about model parameters and inference data are intensifying. Existing Privac…
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
Dun Zeng, Zheshun Wu, Shiyu Liu +3
Federated Learning (FL) is a distributed learning approach that trains machine learning models across multiple devices while keeping their local data private. However, FL often fac…
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
Dun Zeng, Zenglin Xu, Shiyu Liu +3
Federated averaging (FedAvg) is the most fundamental algorithm in Federated learning (FL). Previous theoretical results assert that FedAvg convergence and generalization degenerate…
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu, Shaogao Lv, Dun Zeng +3
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their pri…
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
Dun Zeng, Shiyu Liu, Siqi Liang +4
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leak…