Privacy-Preserving Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems

arXiv:2404.10255v1 Announce Type: cross
Abstract: On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI services 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 …

ai services applications artificial artificial intelligence arxiv as-a-service challenges concept cs.cr cs.dc cs.lg data decentralized deployment device devices end intelligence nature privacy problems real run sensitive servers service services training