{"total":88,"rows":[{"id":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346,"area":null},{"id":"2102.04170","title":"Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach","created":"2021-02-08","cited_by_count":110,"area":null},{"id":"2111.12444","title":"Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications","created":"2021-11-24","cited_by_count":97,"area":null},{"id":"2101.07511","title":"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge","created":"2021-01-19","cited_by_count":56,"area":null},{"id":"2109.00172","title":"Task-Oriented Communication for Multi-Device Cooperative Edge Inference","created":"2021-09-01","cited_by_count":52,"area":null},{"id":"2103.14272","title":"Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design","created":"2021-03-26","cited_by_count":47,"area":null},{"id":"2503.06027","title":"Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models","created":"2025-03-08","cited_by_count":46,"area":null},{"id":"2405.17053","title":"WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence","created":"2024-05-27","cited_by_count":44,"area":null},{"id":"2107.10996","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","created":"2021-07-23","cited_by_count":40,"area":null},{"id":"2307.09182","title":"Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey","created":"2023-07-18","cited_by_count":40,"area":null},{"id":"2208.03392","title":"Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions","created":"2022-08-05","cited_by_count":35,"area":null},{"id":"2211.07893","title":"Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges","created":"2022-11-15","cited_by_count":34,"area":null},{"id":"2207.00969","title":"Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI","created":"2022-07-03","cited_by_count":33,"area":null},{"id":"2312.00333","title":"Green Edge AI: A Contemporary Survey","created":"2023-12-01","cited_by_count":25,"area":null},{"id":"2112.00925","title":"Context-Aware Online Client Selection for Hierarchical Federated Learning","created":"2021-12-02","cited_by_count":20,"area":null},{"id":"2105.01798","title":"Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence","created":"2021-05-04","cited_by_count":18,"area":null},{"id":"2106.08011","title":"Over-the-Air Decentralized Federated Learning","created":"2021-06-15","cited_by_count":18,"area":null},{"id":"2301.03377","title":"Machine Learning for Large-Scale Optimization in 6G Wireless Networks","created":"2023-01-03","cited_by_count":18,"area":null},{"id":"2312.08667","title":"Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey","created":"2023-12-14","cited_by_count":18,"area":null},{"id":"2101.12051","title":"Edge Federated Learning Via Unit-Modulus Over-The-Air Computation","created":"2021-01-28","cited_by_count":16,"area":null},{"id":"2209.04418","title":"Trustworthy Federated Learning via Blockchain","created":"2022-08-13","cited_by_count":15,"area":null},{"id":"2211.01255","title":"Task-Oriented Over-the-Air Computation for Multi-Device Edge AI","created":"2022-11-02","cited_by_count":14,"area":null},{"id":"2304.14271","title":"A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services","created":"2023-04-13","cited_by_count":14,"area":null},{"id":"2505.01821","title":"Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey","created":"2025-05-03","cited_by_count":14,"area":null},{"id":"2104.12678","title":"Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data","created":"2021-04-26","cited_by_count":12,"area":null},{"id":"2112.10313","title":"Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity","created":"2021-12-20","cited_by_count":12,"area":null},{"id":"2205.01423","title":"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2202.05800","title":"SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing","created":"2022-02-11","cited_by_count":11,"area":null},{"id":"2203.15488","title":"Over-the-Air Federated Learning via Second-Order Optimization","created":"2022-03-29","cited_by_count":11,"area":null},{"id":"2205.10635","title":"SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments","created":"2022-05-21","cited_by_count":11,"area":null},{"id":"2401.09656","title":"Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks","created":"2024-01-18","cited_by_count":11,"area":null},{"id":"2403.02619","title":"Training Machine Learning models at the Edge: A Survey","created":"2024-03-05","cited_by_count":11,"area":null},{"id":"2112.08761","title":"DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems","created":"2021-12-16","cited_by_count":10,"area":null},{"id":"2310.10089","title":"Over-the-Air Federated Learning and Optimization","created":"2023-10-16","cited_by_count":10,"area":null},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2107.09518","title":"Relay-Assisted Cooperative Federated Learning","created":"2021-07-20","cited_by_count":9,"area":null},{"id":"2306.17170","title":"An Overview on Generative AI at Scale with Edge-Cloud Computing","created":"2023-06-02","cited_by_count":9,"area":null},{"id":"2603.16952","title":"Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies","created":"2026-03-16","cited_by_count":9,"area":null},{"id":"2203.05468","title":"CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization","created":"2022-03-10","cited_by_count":7,"area":null},{"id":"2305.06279","title":"Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization","created":"2023-05-04","cited_by_count":7,"area":null},{"id":"2310.17944","title":"A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability","created":"2023-10-27","cited_by_count":7,"area":null},{"id":"2405.09514","title":"Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck","created":"2024-05-15","cited_by_count":7,"area":null},{"id":"2105.00591","title":"Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation","created":"2021-05-03","cited_by_count":6,"area":null},{"id":"2303.10434","title":"Byzantine-Resilient Federated Learning at Edge","created":"2023-03-18","cited_by_count":6,"area":null},{"id":"2310.13033","title":"LASER: Linear Compression in Wireless Distributed Optimization","created":"2023-10-19","cited_by_count":6,"area":null},{"id":"2203.17028","title":"Differentially Private Federated Learning via Reconfigurable Intelligent Surface","created":"2022-03-31","cited_by_count":5,"area":null},{"id":"2404.06007","title":"Collaborative Edge AI Inference over Cloud-RAN","created":"2024-04-09","cited_by_count":5,"area":null},{"id":"2203.13517","title":"Sparse Federated Learning with Hierarchical Personalized Models","created":"2022-03-25","cited_by_count":4,"area":null},{"id":"2210.01452","title":"Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control","created":"2022-10-04","cited_by_count":4,"area":null},{"id":"2407.09873","title":"Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge","created":"2024-07-13","cited_by_count":4,"area":null},{"id":"2503.22982","title":"PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference","created":"2025-03-29","cited_by_count":4,"area":null},{"id":"2104.08314","title":"High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks","created":"2021-04-16","cited_by_count":3,"area":null},{"id":"2302.02949","title":"Adaptive Parameterization of Deep Learning Models for Federated Learning","created":"2023-02-06","cited_by_count":3,"area":null},{"id":"2407.11069","title":"Combining Federated Learning and Control: A Survey","created":"2024-07-12","cited_by_count":3,"area":null},{"id":"2108.13009","title":"Communication-Computation Efficient Device-Edge Co-Inference via AutoML","created":"2021-08-30","cited_by_count":2,"area":null},{"id":"2303.15378","title":"CoDeC: Communication-Efficient Decentralized Continual Learning","created":"2023-03-27","cited_by_count":2,"area":null},{"id":"2308.08792","title":"Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks","created":"2023-08-17","cited_by_count":2,"area":null},{"id":"2404.13348","title":"Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems","created":"2024-04-20","cited_by_count":2,"area":null},{"id":"2406.08115","title":"Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey","created":"2024-06-12","cited_by_count":2,"area":null},{"id":"2507.15256","title":"Optimal Transceiver Design in Over-the-Air Federated Distillation","created":"2025-07-21","cited_by_count":2,"area":null},{"id":"2510.01439","title":"Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2206.06679","title":"Matching Pursuit Based Scheduling for Over-the-Air Federated Learning","created":"2022-06-14","cited_by_count":1,"area":null},{"id":"2206.07631","title":"Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning","created":"2022-06-15","cited_by_count":1,"area":null},{"id":"2301.12065","title":"Decentralized Entropic Optimal Transport for Distributed Distribution Comparison","created":"2023-01-28","cited_by_count":1,"area":null},{"id":"2301.12355","title":"Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction","created":"2023-01-29","cited_by_count":1,"area":null},{"id":"2305.10852","title":"Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2312.01288","title":"Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul","created":"2023-12-03","cited_by_count":1,"area":null},{"id":"2312.09259","title":"Livestock feeding behaviour: A review on automated systems for ruminant monitoring","created":"2023-12-03","cited_by_count":1,"area":null},{"id":"2403.10569","title":"Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment","created":"2024-03-14","cited_by_count":1,"area":null},{"id":"2410.05349","title":"SoK: Towards Security and Safety of Edge AI","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2502.15472","title":"Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence","created":"2025-02-21","cited_by_count":1,"area":null},{"id":"2505.02877","title":"A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition","created":"2025-05-05","cited_by_count":1,"area":null},{"id":"2509.07342","title":"FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning","created":"2025-09-09","cited_by_count":1,"area":null},{"id":"2102.12165","title":"Efficient Low-Latency Dynamic Licensing for Deep Neural Network Deployment on Edge Devices","created":"2021-02-24","cited_by_count":0,"area":null},{"id":"2302.00623","title":"Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks","created":"2023-01-12","cited_by_count":0,"area":null},{"id":"2401.05355","title":"Developing a Resource-Constraint EdgeAI model for Surface Defect Detection","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2404.15343","title":"Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis","created":"2024-04-11","cited_by_count":0,"area":null},{"id":"2502.10239","title":"Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices","created":"2025-02-14","cited_by_count":0,"area":null},{"id":"2502.11644","title":"InTec: integrated things-edge computing: a framework for distributing machine learning pipelines in edge AI systems","created":"2025-02-17","cited_by_count":0,"area":null},{"id":"2503.08661","title":"Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems","created":"2025-03-11","cited_by_count":0,"area":null},{"id":"2509.23030","title":"DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligence","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2509.24601","title":"CURA: Size Isnt All You Need -- A Compact Universal Architecture for On-Device Intelligence","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2603.02109","title":"Orchestrating Multimodal DNN Workloads in Wireless Neural Processing","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.03146","title":"Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2607.28907","title":"Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2607.29659","title":"GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2609.29395","title":"Concurrent Split Learning Through Stable Client Clustering","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2610.13298","title":"Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}