27 citations · 36 across the 11 of their papers we have counts for
6 papers · 1 filter
LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang, Jiawei Sun, Jie Li +1
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-cons…
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
Jian Zhang, Bowen Li Jie Li, Chentao Wu
In response to legislation mandating companies to honor the \textit{right to be forgotten} by erasing user data, it has become imperative to enable data removal in Vertical Federat…
Temporal Gradient Inversion Attacks with Robust Optimization
Bowen Li, Hanlin Gu, Ruoxin Chen +5
Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged…
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
Shijing Yuan, Hongze Liu, Hongtao Lv +4
Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Ruoxin Chen, Jie Li, Chentao Wu +2
Neural network classifiers are vulnerable to data poisoning attacks, as attackers can degrade or even manipulate their predictions thorough poisoning only a few training samples. H…