35 citations · 35 across the 9 of their papers we have counts for
6 papers · 1 filter
Timely Parameter Updating in Over-the-Air Federated Learning
Jiaqi Zhu, Zhongyuan Zhao, Xiao Li +3
Incorporating over-the-air computations (OAC) into the model training process of federated learning (FL) is an effective approach to alleviating the communication bottleneck in FL…
Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning
Eunjeong Jeong, Giovanni Perin, Howard H. Yang +1
Federated Learning (FL) on resource-constrained edge devices faces a critical challenge: The computational energy required for training Deep Neural Networks (DNNs) often dominates…
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
Kun Guo, Xuefei Li, Xijun Wang +3
Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices w…
Rethinking Federated Learning Over the Air: The Blessing of Scaling Up
Jiaqi Zhu, Bikramjit Das, Yong Xie +2
Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communic…
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping
Jiaxing Li, Zihan Chen, Kai Fong Ernest Chong +3
Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superpos…
Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT
Jiao Chen, Jiayi He, Fangfang Chen +6
Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal…