Edge Computing

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Overview

Edge computing studies how to process data on or near devices such as phones, sensors, vehicles, robots, and nearby servers instead of sending everything to a distant cloud. Work spans task offloading and resource scheduling, federated and split learning, collaborative edge-cloud inference, and hardware-aware compression and acceleration. Evaluations use workloads from IoT, mobile networks, robotics, and vision, on both simulation benchmarks and real devices such as Raspberry Pis, Jetson boards, and embedded accelerators. The central tension is balancing latency, energy, bandwidth, privacy, and reliability across heterogeneous devices and changing networks; measured deployment costs do not always match gains predicted by models or simulations.

Latest papers

CardsList

199 papers28 papers in the last four weeks, up 33% on the four weeks before. 0.2% of all new papers.

  1. A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System

    Mar 10, 2026Luyao Zou, Hayoung Oh, Chu Myaet Thwal +3Federated Knowledge DistillationFederated Learning

    College of Computing and Informatics, Sungkyunkwan University, Republic of Korea · Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, Republic of Korea · Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali-3814, Bangladesh +2

  2. PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing

    Mar 10, 2026Wei Feng, Jingbo Zhang, Qiong Wu +2Computation OffloadingEdge Computing

    School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China · State Key Laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China · Qualcomm, San Jose, CA 95110, USA

  3. Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices

    Mar 9, 2026Ivan Zaino, Matteo Risso, Daniele Jahier Pagliari +33DGS Training3D Gaussian Splatting

    Politecnico di Torino · VERSES

  4. SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution

    Mar 7, 2026YueMing Zhang, Shuai Xu, Zhengxiong Li +3Multi-Robot CoordinationEdge Computing

    California State University, Long Beach, USA · Case Western Reserve University, USA · University of Colorado Denver, USA +2

  5. Trainable Bitwise Soft Quantization for Input Feature Compression

    Mar 5, 2026Karsten Schrödter, Jan Stenkamp, Nina Herrmann +1Neural Network QuantizationQuantization-Aware Training

    University of Münster (Germany) · University of Copenhagen (Denmark)

  6. Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads

    Mar 4, 2026Subhadip MitraComputation OffloadingLEO Satellite Networks

    RotaStellar

  7. Lambdas at the Far Edge: a Tale of Flying Lambdas and Lambdas on Wheels

    Mar 4, 2026Giorgio Audrito, Daniele Bortoluzzi, Ferruccio Damiani +13Edge Computing

    Department of Computer Science · Department of Computer Science (DI), University of Turin, Turin, Italy · MITO Technology, Milan, Italy +12

  8. Hybrid Orchestration of Edge AI and Microservices via Graph-based Self-Imitation Learning

    Mar 3, 2026Chen Yang, Jin Zheng, Yang Zhuolin +4Edge Computing

    School of Computer Science, South-Central Minzu University, Wuhan, China, 430074, and Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission · Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan, China, 430074 · Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore

  9. Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems

    Mar 3, 2026Mengru Wu, Jiawei Li, Jiaqi Wei +3Distributed InferenceEdge Computing

    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China · School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China · Department of Electronic and Electrical Engineering, University College London, WC1E 7JE London, U.K. +2

  10. Characterizing VLA Models: Identifying the Action Generation Bottleneck for Edge AI Architectures

    Mar 1, 2026Manoj Vishwanathan, Suvinay Subramanian, Anand RaghunathanVision-Language-Action ModelsEfficient VLA Model Inference

    Google, Mountain View, CA USA · Purdue University, West Lafayette, IN USA

  11. BiKA: Kolmogorov-Arnold-Network-inspired Ultra Lightweight Neural Network Hardware Accelerator

    Feb 26, 2026Yuhao Liu, Salim Ullah, Akash KumarNeural Network Hardware AccelerationEfficient Neural Network Inference

    Ruhr University Bochum, Germany · Dresden University of Technology, Germany · Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig), Germany

  12. Accelerating Local LLMs on Resource-Constrained Edge Devices via Distributed Prompt Caching

    Feb 26, 2026Hiroki Matsutani, Naoki Matsuda, Naoto SugiuraLLM Inference AccelerationOn-Device Language Model Inference

    Dept. of ICS, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan

  13. SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference

    Feb 25, 2026Qunyou Liu, Pengbo Yu, Marina Zapater +1Hardware-Aware QuantizationNeural Network Quantization

    Embedded Systems Laboratory (ESL), EPFL, Switzerland · University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland

  14. Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation

    Feb 24, 2026Sales Aribe, Gil Nicholas CagandeFederated LearningCommunication-Efficient Distributed Training

    Information Technology Department, Bukidnon State University, Malaybalay City, Philippines

  15. Carbon-aware decentralized dynamic task offloading in MIMO-MEC networks via multi-agent reinforcement learning

    Feb 21, 2026Mubshra Zulfiqar, Muhammad Ayzed Mirza, Basit QureshiComputation OffloadingCarbon-Aware Computing

    School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430000, Hubei, China · School of Computer Science and Information Engineering, Qilu Institute of Technology, Jinan, 250200, Shandong, China · Department of Computer Science, Prince Sultan University, Riyadh, 12435, Riyadh Province, Saudi Arabia

  16. Node Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AI

    Feb 18, 2026Eiman Kanjo, Mustafa AslanovDecentralized LearningEdge Computing

    Nottingham Trent University, Nottingham, UK · Imperial College London, London, UK

  17. Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation

    Feb 18, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciWireless Resource AllocationFederated RL

    School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

  18. Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting

    Jan 29, 2026Chuan-Chi LaiMulti-Objective Reinforcement LearningFederated RL

    Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan

  19. Chisme: Heterogeneity-Aware Gossip Learning

    May 14, 2025Harikrishna Kuttivelil, Katia ObraczkaEdge ComputingDecentralized Learning

    Computer Science & Engineering University of California Santa Cruz, California, USA