Federated Split Learning for Distributed Intelligence with Resource-Constrained Devices

• Published in: 2024 IEEE International Conference on Communications Workshops (ICC Workshops) pp. 798 - 803
• Main Authors: Ao, Huiqing, Tian, Hui, Ni, Wanli
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.06.2024
• Subjects: Benchmark testing; Conferences; device scheduling; edge intelligence; Energy consumption; Federated split learning; Internet of Things; Processor scheduling; resource allocation; Simulation; Training; Wireless networks

As a distributed machine learning paradigm, federated learning usually requires all edge devices to collaboratively train a large-size artificial intelligence model at local. However, this imposes challenges for these resource-constrained Internet of Things (IoT) devices. Moreover, the communication...