1 citations · 1 across the 10 of their papers we have counts for
11 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…
Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba
Zhenxing Xu, Zeyuan Ma, Weidong Bao +4
We study efficiency as a first-class objective in Neural Combinatorial Optimization (NCO) and present ECO, an efficient learning framework that combines batched preference optimiza…
Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation
Zhenxing Xu, Yihong Lu, Weidong Bao +6
Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision. While Multimodal Large Language Models (MLLMs) offer s…
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,…
AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
Zhenxing Xu, Yizhe Zhang, Weidong Bao +6
Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibit…
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…
Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks
Jingxuan Zhou, Weidong Bao, Ji Wang +3
Federated learning has been extensively studied and applied due to its ability to ensure data security in distributed environments while building better models. However, clients pa…
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…
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
Wenzheng Jiang, Ji Wang, Xiongtao Zhang +3
Federated Reinforcement Learning (FedRL) improves sample efficiency while preserving privacy; however, most existing studies assume homogeneous agents, limiting its applicability i…