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Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). However, existing FU methods suffer from inefficiencies due to the high costs associated with gradient recomputation and storage. This ...

Dataset distillation aims to replace large training sets with significantly smaller synthetic sets while preserving essential information. This method reduces the training costs of advanced deep learning models and is widely used in the image domain. ...

Dataset distillation, also known as dataset condensation, offers a possibility for compressing a large-scale dataset into a small-scale one (i.e., distilled dataset) while achieving similar performance during model training. This method effectively ...

Dataset Distillation (DD) is a technique for synthesizing smaller, compressed datasets from large original datasets while retaining essential information to maintain efficacy. Efficient DD is a current research focus among scholars. Squeeze, Recover and ...

Edge servers and mobile devices are often assigned a large number of computing tasks. However, the data involved in computing tasks is often sensitive in terms of privacy. Our initial proposal is a federated edge learning strategy based on real-world ...

In real-world search settings, learning to rank (LtR) models are trained and tuned repeatedly using large amounts of data, thus consuming significant time and computing resources, and raising efficiency and sustainability concerns. One way to address ...

The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining tasks, incurs substantial computational resource costs when dealing with ...

The authors review a coreset discovery problem and three main methods to solve it: geometric coreset estimation, coreset discovery using the genetic algorithm, and coreset discovery using neural networks. They analyze each of these methods and ...

The development of machine learning solutions often relies on training using large labeled datasets. This raises challenges in terms of data storage, data privacy protection, and longer model training time. One of the possible solutions to overcome these ...