11 citations · 34 across the 10 of their papers we have counts for
5 papers · 1 filter
PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks
Shiwei Ding, Lan Zhang, Miao Pan +1
Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collab…
Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning
Gaurav Bagwe, Xiaoyong Yuan, Miao Pan +1
Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients. This paper focuses on rehearsal-free FCL, which has sev…
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Hong Huang, Lan Zhang, Chaoyue Sun +3
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. Howeve…
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer
Madhureeta Das, Xianhao Chen, Xiaoyong Yuan +1
Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source d…
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models
Lan Zhang, Dapeng Wu, Xiaoyong Yuan
Federated learning enables multiple distributed devices to collaboratively learn a shared prediction model without centralizing their on-device data. Most of the current algorithms…