Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design

--Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy-convergence model capturing the coupled impact of these components, and develop a Bayesian optimization(BO)- based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. This work of Xiangwang Hou was supported by the National Natural Science Foundation of China under grant No. 623B2060. This work of Jingjing Wang was partly supported by the National Natural Science Foundation of China under Grant No. 62222101 and No. U24A20213, partly supported by the Beijing Natural Science Foundation under Grants No. L232043 and No. L222039, partly supported by the Natural Science Foundation of Zhejiang Province under Grant No. LMS25F010007 and partly supported by the Fundamental Research Funds for the Central Universities. This work of Jun Du was partly supported by the National Natural Science Foundation China under Grants No. 62422109 and No.U23A20281.