25 citations · 62 across the 8 of their papers we have counts for
7 papers · 1 filter
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao, Jianyi Zhang, Junda Wu +11
Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy…
SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Zhixu Du, Shiyu Li, Yuhao Wu +7
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable…
Rethinking Normalization Methods in Federated Learning
Zhixu Du, Jingwei Sun, Ang Li +4
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover extern…
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Jianyi Zhang, Ang Li, Minxue Tang +6
Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each…
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
Jingyu Pan, Chen-Chia Chang, Zhiyao Xie +5
Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has b…
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Jingwei Sun, Ang Li, Louis DiValentin +3
Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent wo…
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Binghui Wang, Jiayi Guo, Ang Li +2
Learning with graphs has attracted significant attention recently. Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-re…