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IG-3D:Integrated-Gradients 3D Optimization for Private Transformer Inference 认领 引用
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作者 Lei Sun Jingwen Wang +3 位作者 Peng Hu Xiuqing Mao Cuiyun Hu Zhihong Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1158-1176,共19页
Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose... Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies. 展开更多
关键词 Private inference transformer attention-head pruning integrated gradients transformer model optimization
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An Efficient Federated Learning Optimization Approach Based on Adaptive Hybrid Model Pruning 认领 引用
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作者 MengDie Hu Na Wang +2 位作者 XueHui Du BaiDong Huang KaiYuan Wang 《Computers, Materials & Continua》 SCIE EI 2026年第9期857-881,共25页
With the rapid development of the Internet of Things(IoT)and edge intelligence,the volume of data generated by edge devices has grown explosively.Federated learning(FL),characterized by the paradigm of“data remaining... With the rapid development of the Internet of Things(IoT)and edge intelligence,the volume of data generated by edge devices has grown explosively.Federated learning(FL),characterized by the paradigm of“data remaining local while models are shared,”has emerged as a key approach for adapting to the distributed architecture of edge computing,breaking down data silos,and enabling privacy preservation.However,its practical deployment in edge computing environments still faces significant challenges,including limited device resources and pronounced data heterogeneity.Existing pruning strategies for federated learning are predominantly based on static and single-design schemes,making it difficult to achieve a balanced trade-off among training overhead,communication cost,and model accuracy.To address these issues,this paper proposes FedAHP(Federated Learning with Adaptive Hybrid Pruning),an efficiency optimization scheme for federated learning based on adaptive hybrid model pruning.On the client side,an adaptive pruning mechanism driven by training states is designed to dynamically adjust pruning behavior during local training.On the server side,a counter-based heterogeneous aggregation method is adopted to efficiently align updates from clients with different pruning rates,thereby avoiding additional communication overhead.Furthermore,after training becomes stable,a performance-aware periodic structured pruning strategy is introduced to compress the global model scale and reduce subsequent training costs.Experimental results demonstrate that FedAHP maintains high model accuracy on the MNIST,CIFAR-10 and CIFAR-100 datasets while significantly reducing per-round communication overhead and time cost,making it well suited to the resource-constrained requirements of edge computing scenarios. 展开更多
关键词 Federated learning edge computing model pruning efficiency optimization
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