A framework reforming personalized Internet of Things by federated meta-learning.

ABSTRACT: Advances in Artificial Intelligence envision a promising future, where the personalized Internet of Things can be revolutionized with the ability to continuously improve system efficiency and service quality. However, with the introduction of laws and regulations about data security and privacy protection, centralized solutions, which require data to be collected and processed directly on a central server, become impractical for personalized Internet of Things to train Artificial Intelligence models for a variety of domain-specific scenarios. Motivated by this, this paper introduces Cedar, a secure, cost-efficient and domain-adaptive framework to train personalized models in a crowdsourcing-based and privacy-preserving manner. In essentials, Cedar integrates federated learning and meta-lea

SUBMITTER: You L 

PROVIDER: S-EPMC12009980 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature