7 papers
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
Xiufang Shi, Wei Zhang, Yuheng Li +5
In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-indepen…
Exemplar-condensed Federated Class-incremental Learning
Rui Sun, Yumin Zhang, Varun Ojha +4
We propose Exemplar-Condensed federated class-incremental learning (ECoral) to distil the training characteristics of real images from streaming data into informative rehearsal exe…
Rehearsal-free Federated Domain-incremental Learning
Rui Sun, Haoran Duan, Jiahua Dong +3
We introduce a rehearsal-free federated domain incremental learning framework, RefFiL, based on a global prompt-sharing paradigm to alleviate catastrophic forgetting challenges in…
D2Fusion: Dual-domain Fusion with Feature Superposition for Deepfake Detection
Xueqi Qiu, Xingyu Miao, Fan Wan +5
Deepfake detection is crucial for curbing the harm it causes to society. However, current Deepfake detection methods fail to thoroughly explore artifact information across differen…
Genomic data processing with GenomeFlow
Junseok Park, Eduardo A. Maury, Changhoon Oh +3
Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure…
A Circular Construction Product Ontology for End-of-Life Decision-Making
Kwabena Adu-Duodu, Stanly Wilson, Yinhao Li +8
Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hi…