2 citations · 4 across the 28 of their papers we have counts for
9 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…
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…
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…
PAGE: Parametric Generative Explainer for Graph Neural Network
Yang Qiu, Wei Liu, Jun Wang +1
This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitati…
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…
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…
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…