36 citations · 71 across the 5 of their papers we have counts for
5 papers
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
Dezhong Yao, Tongtong Liu, Qi Cao +1
Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN)…
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
Dezhong Yao, Wanning Pan, Michael J O'Neill +4
One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for d…
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…
Sparse online relative similarity learning
Dezhong Yao, Peilin Zhao, Chen Yu +2
For many data mining and machine learning tasks, the quality of a similarity measure is the key for their performance. To automatically find a good similarity measure from datasets…
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition
Zhenzhong Lan, Dezhong Yao, Ming Lin +2
Motivated by the success of data-driven convolutional neural networks (CNNs) in object recognition on static images, researchers are working hard towards developing CNN equivalents…