2 papers
cs.RO2025
Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Wanli Ni, Hui Tian, Shuai Wang +3
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart f…
cs.IT2025
Accuracy and Security-Guaranteed Participant Selection and Beamforming Design for RIS-Assisted Federated Learning
Mengru Wu, Yu Gao, Weidang Lu +3
Federated learning (FL) has emerged as an effective approach for training neural network models without requiring the sharing of participants' raw data, thereby addressing data pri…