75 citations · 228 across the 18 of their papers we have counts for
7 papers · 1 filter
Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation
Yixin Liu, Chenrui Fan, Pan Zhou +1
While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data fo…
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Kai Zhang, Yutong Dai, Hongyi Wang +3
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…
Transferable Unlearnable Examples
Jie Ren, Han Xu, Yuxuan Wan +3
With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parti…
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
Yu Wang, Zhiwei Liu, Ziwei Fan +2
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold…
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data
Dezhong Yao, Wanning Pan, Yutong Dai +5
Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data. However, du…
Subgraph Federated Learning with Missing Neighbor Generation
Ke Zhang, Carl Yang, Xiaoxiao Li +2
Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and b…