25 citations · 87 across the 56 of their papers we have counts for
17 papers · 1 filter
FedGIG: Graph Inversion from Gradient in Federated Learning
Tianzhe Xiao, Yichen Li, Yining Qi +2
Recent studies have shown that Federated learning (FL) is vulnerable to Gradient Inversion Attacks (GIA), which can recover private training data from shared gradients. However, ex…
Deploying Foundation Model Powered Agent Services: A Survey
Wenchao Xu, Jinyu Chen, Peirong Zheng +8
Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intel…
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Yichen Li, Haozhao Wang, Wenchao Xu +9
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-st…
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
Yichen Li, Yuying Wang, Haozhao Wang +3
Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel concepts from continuously shifting training data while avoiding knowledge forgetting o…
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Wei Liu, Zhiying Deng, Zhongyu Niu +4
An important line of research in the field of explainability is to extract a small subset of crucial rationales from the full input. The most widely used criterion for rationale ex…
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Shiwei Li, Zhuoqi Hu, Xing Tang +6
Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…
Masked Random Noise for Communication Efficient Federated Learning
Shiwei Li, Yingyi Cheng, Haozhao Wang +7
Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…
Adversarial Attack for Explanation Robustness of Rationalization Models
Yuankai Zhang, Lingxiao Kong, Haozhao Wang +4
Rationalization models, which select a subset of input text as rationale-crucial for humans to understand and trust predictions-have recently emerged as a prominent research area i…
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang +7
Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…
Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
Fushuo Huo, Wenchao Xu, Zhong Zhang +3
While Large Vision-Language Models (LVLMs) have rapidly advanced in recent years, the prevalent issue known as the `hallucination' problem has emerged as a significant bottleneck,…
Detached and Interactive Multimodal Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang +2
Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimo…
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
Yichen Li, Wenchao Xu, Haozhao Wang +3
This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client continues to learn incremental tasks where their domain shifts from each other. We propose a no…
Dual Expert Distillation Network for Generalized Zero-Shot Learning
Zhijie Rao, Jingcai Guo, Xiaocheng Lu +5
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping…
Towards Efficient Replay in Federated Incremental Learning
Yichen Li, Qunwei Li, Haozhao Wang +3
In Federated Learning (FL), the data in each client is typically assumed fixed or static. However, data often comes in an incremental manner in real-world applications, where the d…
OTAS: An Elastic Transformer Serving System via Token Adaptation
Jinyu Chen, Wenchao Xu, Zicong Hong +4
Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requireme…
Disentangle Estimation of Causal Effects from Cross-Silo Data
Yuxuan Liu, Haozhao Wang, Shuang Wang +4
Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be d…
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection
Yunfeng Fan, Wenchao Xu, Haozhao Wang +3
Selecting proper clients to participate in each federated learning (FL) round is critical to effectively harness a broad range of distributed data. Existing client selection method…