  On-device intelligence (ODI) enables artificial intelligence (AI)
applications to run on end devices, providing real-time and customized AI
inference without relying on remote servers. However, training models for
on-device deployment face significant challenges due to the decentralized and
privacy-sensitive nature of users' data, along with end-side constraints
related to network connectivity, computation efficiency, etc. Existing training
paradigms, such as cloud-based training, federated learning, and transfer
learning, fail to sufficiently address these practical constraints that are
prevalent for devices. To overcome these challenges, we propose
Privacy-Enhanced Training-as-a-Service (PTaaS), a novel service computing
paradigm that provides privacy-friendly, customized AI model training for end
devices. PTaaS outsources the core training process to remote and powerful
cloud or edge servers, efficiently developing customized on-device models based
on uploaded anonymous queries, enhancing data privacy while reducing the
computation load on individual devices. We explore the definition, goals, and
design principles of PTaaS, alongside emerging technologies that support the
PTaaS paradigm. An architectural scheme for PTaaS is also presented, followed
by a series of open problems that set the stage for future research directions
in the field of PTaaS.
