Основні поняття
PTaaS is a novel service computing paradigm that outsources the training of customized AI models to remote cloud or edge servers, enabling efficient development of high-performance on-device models with guaranteed privacy protection.
Анотація
The paper proposes the concept of Privacy-Preserving Training-as-a-Service (PTaaS), a novel service computing paradigm for training AI models to be deployed on end devices. PTaaS aims to address the challenges faced by on-device model training, such as data privacy, network connectivity, and resource constraints, by outsourcing the core training process to remote cloud or edge servers.
Key highlights:
PTaaS only requires devices to provide anonymous information related to local data as part of queries, eliminating the need to share raw data with remote servers, thus ensuring user privacy.
PTaaS leverages the powerful computing resources and abundant data owned by cloud or edge servers to train customized on-device models efficiently, reducing the computation burden on individual devices.
PTaaS simplifies the model training process for end devices, allowing them to flexibly request model updates according to their customized demands.
PTaaS enables fair pricing based on the consumed computing and data resources, ensuring cost-effectiveness for diverse devices and creating profit potential for service providers.
The paper also presents the five-layer hierarchy structure of PTaaS, including the infrastructure, data, algorithm, service, and application layers, and discusses the emerging technologies that support PTaaS, such as privacy computing, cloud-edge collaboration, transfer learning, and information retrieval.
Finally, the paper identifies several open problems that need to be addressed for the practical implementation and widespread adoption of PTaaS, including improving privacy protection mechanisms, optimizing cloud-edge resource management, enhancing customized model training, and establishing standard specifications.
Статистика
"PTaaS aims to protect user privacy by eliminating the need for end devices to share raw data with remote servers."
"PTaaS leverages the powerful computing resources and abundant data owned by cloud or edge servers to train customized on-device models efficiently, reducing the computation burden on individual devices."
"PTaaS simplifies the model training process for end devices, allowing them to flexibly request model updates according to their customized demands."
"PTaaS enables fair pricing based on the consumed computing and data resources, ensuring cost-effectiveness for diverse devices and creating profit potential for service providers."
Цитати
"PTaaS is a novel service computing paradigm that outsources the training of customized AI models to remote cloud or edge servers, enabling efficient development of high-performance on-device models with guaranteed privacy protection."
"PTaaS only requires devices to provide anonymous information related to local data as part of queries, eliminating the need to share raw data with remote servers, thus ensuring user privacy."