C Hu, C Zhang, D Lei, T Wu, X Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the proliferation of machine learning, the cloud server has been employed to collect massive data and train machine learning models. Several privacy-preserving machine …
Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning …
X Zhang, F Li, Z Zhang, Q Li, C Wang… - IEEE INFOCOM 2020 …, 2020 - ieeexplore.ieee.org
Federated learning (FL), as a privacy-preserving machine learning framework, draws growing attention in both industry and academia. It obtains a jointly accurate model by …
Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data …
X Tang, M Shen, Q Li, L Zhu, T Xue… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated learning (FL) protects training data in clients by collaboratively training local machine learning models of clients for a global model, instead of directly feeding the training …
F Song, Z Qin, L Xue, J Zhang, X Lin… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage …
Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are …
Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted. While many …
S Lee, H Ko, J Kim, H Oh - IEEE Transactions on Dependable …, 2024 - ieeexplore.ieee.org
It is becoming important for the client to be able to check whether the AI inference services have been correctly calculated. Since the weight values in a CNN model are assets of …