6 papers
FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters
Feihong Nan, Zhengyi Zhong, Pan Wang +4
Federated unlearning (FU) is critical for complying with legal mandates like the right to be forgotten in decentralized systems, yet current methods face a persistent dilemma betwe…
Gains: Fine-grained Federated Domain Adaptation in Open Set
Zhengyi Zhong, Wenzheng Jiang, Weidong Bao +5
Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios,…
SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices
Zhengyi Zhong, Weidong Bao, Ji Wang +3
The proliferation of end devices has led to a distributed computing paradigm, wherein on-device machine learning models continuously process diverse data generated by these devices…
Efficient Multi-Task Modeling through Automated Fusion of Trained Models
Jingxuan Zhou, Weidong Bao, Ji Wang +2
Although multi-task learning is widely applied in intelligent services, traditional multi-task modeling methods often require customized designs based on specific task combinations…
CUT: Pruning Pre-Trained Multi-Task Models into Compact Models for Edge Devices
Jingxuan Zhou, Weidong Bao, Ji Wang +1
Multi-task learning has garnered widespread attention in the industry due to its efficient data utilization and strong generalization capabilities, making it particularly suitable…
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
Zhengyi Zhong, Weidong Bao, Ji Wang +4
Federated Learning is a promising paradigm for privacy-preserving collaborative model training. In practice, it is essential not only to continuously train the model to acquire new…