Work

DAFL: Device-to-Device Transmissions for Delay-Efficient Federated Learning Over Mobile Devices

Year: 2024

Type: article

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2024.3486545

Authors: Huai-An Su, Pavana Prakash, Rui Chen, Yanmin Gong, Rong Yu, Xin Fu, Miao Pan

Institutions: University of Houston, Hewlett-Packard (United States), The University of Texas at San Antonio, Guangdong University of Technology

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

Funders: National Science Foundation, National Natural Science Foundation of China, Division of Computer and Network Systems

Awards: G230777526, Collaborative Research: CNS Core: Medium: Towards Federated Learning over 5G Mobile Devices: High Efficiency, Low Latency, and Good Privacy, Collaborative Research: CSR: Medium: Enhancing Energy Awareness for Efficient Federated Learning over Mobile AI Systems, G3542784078, Collaborative Research:CISE-MSI:DP:CNS:Enabling On-Demand and Flexible Mobile Edge Computing with Integrated Aerial-Ground Vehicles, G4326547789, CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Networking, G6757976958, Collaborative Research: CNS Core: Medium: Towards Federated Learning over 5G Mobile Devices: High Efficiency, Low Latency, and Good Privacy, Collaborative Research: CISE MSI: RDP: CNS: Towards the Resilient NextG Network Design for Federated Learning over Mobile Devices, G8959921509

OpenAlex ID: W4403759009

FWCI: 0.295

Cites: 19

Cited by: 2

Related to: 10