6 papers
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning
Luis Frentzen Salim, Chia-Chun Wu, Tran Van Nhiem +2
Post-training expert pruning is a promising deployment technique for Mixture-of-Experts (MoE) models. The calibration set is an integral part of the pruning process, however, its c…
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
Xiao Ma, Hong Shen, Hui Tian +2
This paper propose a robust decentralized federated distillation method that enables clients with heterogeneous models to collaborate through predictions on shared unlabeled public…
AEGIS: Attention-Embedding Gradient Isolation Shield - Triple-Channel Gradient Masking for Privacy-Preserving Federated LLM Fine-Tuning
Ye Tao, Hong Shen, Hui Tian +2
Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model training. Through our analysis…
An O(K)-Approximation Coflow Scheduling in K-Core Optical Circuit Switching Networks
Xin Wang, Hong Shen, Hui Tian +1
Coflow has emerged as a fundamental application-layer abstraction in distributed systems, enabling collaborative management of related flows to enhance job completion efficiency. T…
Scheduling Coflows in Multi-Core OCS Networks with Performance Guarantee
Xin Wang, Hong Shen, Hui Tian +1
The coflow abstraction captures application-level communication patterns and enables coordinated scheduling of parallel flows to reduce job completion times in distributed systems.…
Efficient Coflow Scheduling in Hybrid-Switched Data Center Networks
Xin Wang, Hong Shen, Hui Tian
To improve the application-level communication performance, scheduling of coflows, a collection of parallel flows sharing the same objective, is prevalent in modern data center net…