{"product_id":"network-and-parallel-computing-9789819628636","title":"Network and Parallel Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- \u003cstrong\u003eEdge Computing and Intelligence\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- A Fair Cooperation Strategy in the Edge-Edge Collaboration Scenario.\u003c\/p\u003e\u003cp\u003e.- Joint Optimization of Transmission and Computation for Multi-Source MEC System Based on Deep Reinforcement Learning.\u003c\/p\u003e\u003cp\u003e.- MARL-Based Joint Optimization of Service Migration and Resource Allocation in MEC.\u003c\/p\u003e\u003cp\u003e.- NBS-Based Mobile Edge Computing: A Joint Optimization of QoS and Energy in Computation Offloading.\u003c\/p\u003e\u003cp\u003e.- Optimizing Resource Allocation in the Internet of Vehicles: An Intelligent Vehicle-Edge-Cloud Collaboration Approach.\u003c\/p\u003e\u003cp\u003e.- Proactive Intellectual Property Protection for Edge AI Models.\u003c\/p\u003e\u003cp\u003e.- SC-TSDRL: A Cloud-Edge Collaboration Framework for Diffusion Model Inference Acceleration.\u003c\/p\u003e\u003cp\u003e.- Vickrey Auction Offloading for Edge-Assisted Video Analytics with Dynamic Gain Prediction.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eFederated Learning Algorithms and Systems\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- An Efficient Federated Meta Unlearning Algorithm with Enhanced Privacy Protection.\u003c\/p\u003e\u003cp\u003e.- Balanced Federated Learning with Two-stage Client Selection for Internet of Vehicles.\u003c\/p\u003e\u003cp\u003e.- Efficient Federated Learning with Cost-Adjustable Generative AI over Heterogeneous Edge Devices.\u003c\/p\u003e\u003cp\u003e.- FedDeSnowNet: Federated De-snowing Network for LiDAR Point Clouds.\u003c\/p\u003e\u003cp\u003e.- Federal Knowledge Graph Embedding Based on Incentive Mechanism.\u003c\/p\u003e\u003cp\u003e.- FedZipper: A Layer-wise Quantization Compression Framework for Federated Learning with Statistical Heterogeneity.\u003c\/p\u003e\u003cp\u003e.- Heterogeneous Federated Learning with Controlled Gradient Variate of Client Momentum.\u003c\/p\u003e\u003cp\u003e.- No Fear of Domain Discrepancy: One-Shot Federated Learning via Class-Aware Distillation.\u003c\/p\u003e\u003cp\u003e.- Trustworthy and Incentivized Federated Learning Based on Blockchain.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eEmerging Networks\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- A Satellite-Ground Link Handover Strategy in LEO Networks using Advantage Actor-Critic Algorithm.\u003c\/p\u003e\u003cp\u003e.- A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation.\u003c\/p\u003e\u003cp\u003e.- Adaptive Switching of Lightweight and Complex DNNs for Air-Ground Collaborative Intelligence.\u003c\/p\u003e\u003cp\u003e.- ADMM for Energy-efficient Computation Offloading in Marine Mobile Edge Computing Networks.\u003c\/p\u003e\u003cp\u003e.- Analytical Route Discovery Time Estimation for UAV Networks.\u003c\/p\u003e\u003cp\u003e.- Cross-Layer Intrusion Detection in UWSNs Using an Optimized CNN-LSTM Model.\u003c\/p\u003e\u003cp\u003e.- Efficient Task Offloading in MEC via UAV-UGV Collaboration.\u003c\/p\u003e\u003cp\u003e.- SUCP Analysis for Region-Centric UAV-Assisted MEC Networks.\u003c\/p\u003e\u003cp\u003e.- Task Offloading Optimization in Multi-layer LEO Satellite-Terrestrial Integrated Networks with Hybrid Cloud and Edge Computing.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eIn-network Computing and Processing\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- 2FA Sketch: Two-Factor Armor Sketch for Accurate and Efficient Heavy Hitter Detection in Data Streams.\u003c\/p\u003e\u003cp\u003e.- A Large-Scale Study of Abnormal Recursive DNS.\u003c\/p\u003e\u003cp\u003e.- Adaptive Gradient Data Partition and Route Selection for Distributed DNN Training.\u003c\/p\u003e\u003cp\u003e.- DeepSight: In-network packet loss management for TCP applications in datacenter networks.\u003c\/p\u003e\u003cp\u003e.- DTN Routing Algorithm Based on Social Center and Classifier.\u003c\/p\u003e\u003cp\u003e.- E2E-AutoPT: an end-to-end automated penetration testing with LSTM-PPO approach.\u003c\/p\u003e\u003cp\u003e.- EAMTI: A Novel Method Toward Early and Accurate Malicious Traffic Identification.\u003c\/p\u003e\u003cp\u003e.- Gradient-aware Incremental Network Quantization.\u003c\/p\u003e\u003cp\u003e.- Providing Fine-grained Latency Control for Time Sensitive Networking: A Reordering Method.\u003c\/p\u003e\u003cp\u003e.- rpkt: A Generic, Safe, and Efficient Userspace Packet Processing Library in Rust.\u003c\/p\u003e\u003cp\u003e.- RuleAlchemy: Bidirectional Conflict-Aware Rule Aggregation for Crossed Probing Paths in SDN.\u003c\/p\u003e\u003cp\u003e.- RVCC: Congestion Control to Reduce Victim Flows in Data Center Networks.\u003c\/p\u003e\u003cp\u003e.- TAB: Traffic-aware Buffer Management on Programmable Switches.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212873064791,"sku":"9789819628636","price":64.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/network-and-parallel-computing-9789819628636","provider":"Book Curl","version":"1.0","type":"link"}