5 papers
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
Yanxin Yang, Ming Hu, Xiaofei Xie +4
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…
Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation
Yujie Wang, Chao Huang, Liner Yang +5
This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotation quality and reducing costs. Unlike previous studies targeting simpler tasks, this study…
Sampling to Distill: Knowledge Transfer from Open-World Data
Yuzheng Wang, Zhaoyu Chen, Jie Zhang +7
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data…
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination
Ming Hu, Zhihao Yue, Xiaofei Xie +6
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Fede…
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma, Shangqing Liu, Mengjie Zhao +5
Past research has examined how well these models grasp code syntax, yet their understanding of code semantics still needs to be explored. We extensively analyze seven code models t…