Awesome Edge Computing

Key papers

• LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data (2025)
  Yuxin Zhang et al.
  5.84
• AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks (2024)
  Zheng Lin et al.
  5.80
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks (2025)
  Shuai Wang et al.
  4.76
• Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks (2023)
  Payam Abdisarabshali et al.
  4.52
• QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning (2026)
  Nazmus Shakib Shadin et al.
  4.39
• Building a privacy-preserving Federated Recommender system for mobile devices (2026)
  Aasheesh Singh
  4.33
• Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence (2026)
  Xianke Qiang et al.
  4.33
• Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System (2026)
  Cheng-Wei Ching et al.
  4.33
• Autonomic Federated-Market Orchestration for the Edge-Cloud Continuum (2026)
  Lauri Lov\'en et al.
  4.33
• Efficient Deployment of Large Language Models on Resource-constrained Devices (2025)
  Zhiwei Yao et al.
  4.25
• Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment (2025)
  Xubin Wang et al.
  4.25
• Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching (2025)
  Krishnendu S. Tharakan et al.
  4.25
• Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing (2025)
  Minghong Fang et al.
  4.25
• Hierarchical Split Federated Learning: Convergence Analysis and System Optimization (2024)
  Zheng Lin et al.
  4.19
• FedICT: Federated Multi-task Distillation for Multi-access Edge Computing (2023)
  Zhiyuan Wu et al.
  4.13
• Federated Learning in Practice: Reflections and Projections (2024)
  Katharine Daly et al.
  4.08
• Floe: Federated Specialization for Real-Time LLM-SLM Inference (2026)
  Chunlin Tian et al.
  4.04
• Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT (2025)
  Anas Ali et al.
  3.81
• Enhancing Efficiency in Multidevice Federated Learning through Data Selection (2022)
  Fan Mo et al.
  3.71
• Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering (2022)
  Shahryar Zehtabi et al.
  3.71
• FedRIR: Rethinking Information Representation in Federated Learning (2025)
  Yongqiang Huang et al.
  3.64
• Distributed Learning and Inference Systems: A Networking Perspective (2025)
  Hesham G. Moussa et al.
  3.59
• Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control (2023)
  Minh Ngoc Luu et al.
  3.42
• Scaling Real-Time Traffic Analytics on Edge-Cloud Fabrics for City-Scale Camera Networks (2026)
  Akash Sharma et al.
  3.34
• Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application (2024)
  Anwesha Mukherjee et al.
  3.31
• Trustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized, Verifiable, and Incentive-Aligned Coordination (2025)
  Pius Onobhayedo et al.
  3.21
• Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons (2025)
  Mohamad Abou Ali et al.
  3.15
• LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning (2024)
  Shixiong Qi et al.
  3.14
• SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning (2025)
  Dung T. Tran et al.
  3.04
• FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm (2025)
  Shreya Ghosh et al.
  3.04
• FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning (2025)
  Baran Can G\"ul et al.
  2.99
• FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture (2025)
  Somayeh Sobati-M
  2.99
• Energy, Scalability, Data and Security in Massive IoT: Current Landscape and Future Directions (2025)
  Imane Cheikh et al.
  2.87
• Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning (2025)
  Chongyang Tan et al.
  2.87
• Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks (2025)
  Milad Rahmati
  2.82
• EcoLearn: Optimizing the Carbon Footprint of Federated Learning (2023)
  Talha Mehboob et al.
  2.75
• Using Federated Machine Learning in Predictive Maintenance of Jet Engines (2025)
  Asaph Matheus Barbosa et al.
  2.71
• Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT (2025)
  Robert Akinie et al.
  2.71
• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities (2025)
  Milad Rahmati
  2.71
• Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection (2025)
  Hongrui Shi and Valentin Radu and Po Yang
  2.65
• Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design (2025)
  Saba Asaad et al.
  2.65
• EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients (2024)
  Meihan Wu et al.
  2.60
• Exploiting Label Skewness for Spiking Neural Networks in Federated Learning (2024)
  Di Yu et al.
  2.60
• Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing (2024)
  Fardin Jalil Piran et al.
  2.54
• Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices (2024)
  Kilian Pfeiffer et al.
  2.54
• On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients (2024)
  Satish Kumar Keshri et al.
  2.54
• Exploiting Features and Logits in Heterogeneous Federated Learning (2022)
  Yun-Hin Chan et al.
  2.53
• A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing (2024)
  Anwesha Mukherjee et al.
  2.43
• FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection (2024)
  Jiaxiang Geng et al.
  2.32
• Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing (2024)
  Cui Zhang et al.
  2.32
• Swarm Learning: A Survey of Concepts, Applications, and Trends (2024)
  Elham Shammar et al.
  2.21
• Robust Decentralized Learning with Local Updates and Gradient Tracking (2024)
  Sajjad Ghiasvand et al.
  2.21
• Privacy-aware Berrut Approximated Coded Computing for Federated Learning (2024)
  Xavier Mart\'inez Lua\~na et al.
  2.21
• Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems (2024)
  Zhiyuan Wu et al.
  2.15
• Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training (2024)
  Yebo Wu et al.
  2.15
• FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update (2024)
  Ziru Niu et al.
  2.10
• Resource-efficient Layer-wise Federated Self-supervised Learning (2024)
  Ye Lin Tun et al.
  1.99
• Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments (2023)
  Surojit Ganguli et al.
  1.93
• RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge (2026)
  Beining Wu et al.
  1.89
• SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport (2026)
  Zheng Jiang et al.
  1.89