{"total":352,"rows":[{"id":"2102.13451","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","created":"2021-02-26","cited_by_count":192,"area":null},{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184,"area":null},{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2104.14380","title":"PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments","created":"2021-04-29","cited_by_count":93,"area":null},{"id":"2105.05883","title":"Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning","created":"2021-05-12","cited_by_count":93,"area":null},{"id":"2309.15698","title":"Deep Model Fusion: A Survey","created":"2023-09-27","cited_by_count":91,"area":null},{"id":"2108.11887","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","created":"2021-08-26","cited_by_count":81,"area":null},{"id":"2106.07820","title":"On Large-Cohort Training for Federated Learning","created":"2021-06-15","cited_by_count":74,"area":null},{"id":"2206.00187","title":"DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training","created":"2022-06-01","cited_by_count":74,"area":null},{"id":"2112.11256","title":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","created":"2021-12-21","cited_by_count":66,"area":null},{"id":"2211.01549","title":"Client Selection in Federated Learning: Principles, Challenges, and Opportunities","created":"2022-11-03","cited_by_count":62,"area":null},{"id":"2111.01872","title":"Towards Fairness-Aware Federated Learning","created":"2021-11-02","cited_by_count":58,"area":null},{"id":"2108.08842","title":"EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning","created":"2021-08-19","cited_by_count":55,"area":null},{"id":"2205.02719","title":"Communication-Efficient Adaptive Federated Learning","created":"2022-05-05","cited_by_count":55,"area":null},{"id":"2101.12704","title":"Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis","created":"2021-01-29","cited_by_count":51,"area":null},{"id":"2201.01601","title":"FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients","created":"2022-01-05","cited_by_count":46,"area":null},{"id":"2210.04505","title":"A Survey on Heterogeneous Federated Learning","created":"2022-10-10","cited_by_count":42,"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":"2308.10407","title":"Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges","created":"2023-08-21","cited_by_count":40,"area":null},{"id":"2105.07066","title":"Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data","created":"2021-05-14","cited_by_count":39,"area":null},{"id":"2106.15406","title":"A Comprehensive Survey of Incentive Mechanism for Federated Learning","created":"2021-06-27","cited_by_count":38,"area":null},{"id":"2105.03170","title":"FedGL: Federated Graph Learning Framework with Global Self-Supervision","created":"2021-05-07","cited_by_count":34,"area":null},{"id":"2209.15245","title":"Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction","created":"2022-09-30","cited_by_count":32,"area":null},{"id":"2109.03775","title":"FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models","created":"2021-09-08","cited_by_count":31,"area":null},{"id":"2201.02932","title":"A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning","created":"2022-01-09","cited_by_count":31,"area":null},{"id":"2106.04405","title":"Federated Neural Collaborative Filtering","created":"2021-06-02","cited_by_count":30,"area":null},{"id":"2101.00798","title":"Fusion of Federated Learning and Industrial Internet of Things: A Survey","created":"2021-01-04","cited_by_count":29,"area":null},{"id":"2112.05928","title":"Efficient Device Scheduling with Multi-Job Federated Learning","created":"2021-12-11","cited_by_count":29,"area":null},{"id":"2201.07912","title":"Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization","created":"2022-01-19","cited_by_count":28,"area":null},{"id":"2102.01593","title":"FEDZIP: A Compression Framework for Communication-Efficient Federated Learning","created":"2021-02-02","cited_by_count":27,"area":null},{"id":"2111.01108","title":"Resource-Efficient Federated Learning","created":"2021-11-01","cited_by_count":26,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"area":null},{"id":"2308.13894","title":"FwdLLM: Efficient FedLLM using Forward Gradient","created":"2023-08-26","cited_by_count":26,"area":null},{"id":"2109.05411","title":"Cost-Effective Federated Learning in Mobile Edge Networks","created":"2021-09-12","cited_by_count":25,"area":null},{"id":"2202.01512","title":"Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT","created":"2022-02-03","cited_by_count":25,"area":null},{"id":"2205.10162","title":"FedAdapter: Efficient Federated Learning for Modern NLP","created":"2022-05-20","cited_by_count":25,"area":null},{"id":"2301.00767","title":"A Survey on Federated Recommendation Systems","created":"2022-12-27","cited_by_count":25,"area":null},{"id":"2312.00333","title":"Green Edge AI: A Contemporary Survey","created":"2023-12-01","cited_by_count":25,"area":null},{"id":"2107.10663","title":"Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning","created":"2021-07-21","cited_by_count":23,"area":null},{"id":"2109.09046","title":"Improving Fairness for Data Valuation in Horizontal Federated Learning","created":"2021-09-19","cited_by_count":22,"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":"2206.10546","title":"FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity","created":"2022-06-21","cited_by_count":20,"area":null},{"id":"2211.13430","title":"Multi-Job Intelligent Scheduling with Cross-Device Federated Learning","created":"2022-11-24","cited_by_count":20,"area":null},{"id":"2306.09912","title":"Towards Quantum Federated Learning","created":"2023-06-16","cited_by_count":20,"area":null},{"id":"2409.11585","title":"Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework","created":"2024-09-17","cited_by_count":19,"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":"2209.07124","title":"The Cost of Training Machine Learning Models over Distributed Data Sources","created":"2022-09-15","cited_by_count":18,"area":null},{"id":"2101.11296","title":"FedH2L: Federated Learning with Model and Statistical Heterogeneity","created":"2021-01-27","cited_by_count":17,"area":null},{"id":"2206.10189","title":"A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates","created":"2022-06-21","cited_by_count":17,"area":null},{"id":"2301.06447","title":"HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association","created":"2023-01-16","cited_by_count":17,"area":null},{"id":"2303.10677","title":"A Survey of Federated Learning for Connected and Automated Vehicles","created":"2023-03-19","cited_by_count":17,"area":null},{"id":"2104.07515","title":"FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems","created":"2021-04-15","cited_by_count":16,"area":null},{"id":"2108.00974","title":"Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges","created":"2021-08-02","cited_by_count":16,"area":null},{"id":"2110.06556","title":"Communication-Efficient Online Federated Learning Framework for Nonlinear Regression","created":"2021-10-13","cited_by_count":16,"area":null},{"id":"2204.08069","title":"Self-Aware Personalized Federated Learning","created":"2022-04-17","cited_by_count":16,"area":null},{"id":"2312.12670","title":"On the Role of Server Momentum in Federated Learning","created":"2023-12-19","cited_by_count":16,"area":null},{"id":"2108.03980","title":"Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness","created":"2021-07-30","cited_by_count":15,"area":null},{"id":"2110.02182","title":"A Systematic Survey of Blockchained Federated Learning","created":"2021-10-05","cited_by_count":15,"area":null},{"id":"2112.06694","title":"Optimal Rate Adaption in Federated Learning with Compressed Communications","created":"2021-12-13","cited_by_count":15,"area":null},{"id":"2212.05974","title":"Federated Few-Shot Learning for Mobile NLP","created":"2022-12-12","cited_by_count":15,"area":null},{"id":"2306.02426","title":"Resilient Constrained Learning","created":"2023-06-04","cited_by_count":15,"area":null},{"id":"2307.10738","title":"Fairness-Aware Client Selection for Federated Learning","created":"2023-07-20","cited_by_count":15,"area":null},{"id":"2106.02310","title":"FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning","created":"2021-06-04","cited_by_count":14,"area":null},{"id":"2109.00151","title":"Asynchronous Federated Learning for Sensor Data with Concept Drift","created":"2021-09-01","cited_by_count":14,"area":null},{"id":"2207.09611","title":"Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions","created":"2022-07-20","cited_by_count":14,"area":null},{"id":"2301.00519","title":"Holistic Network Virtualization and Pervasive Network Intelligence for 6G","created":"2023-01-02","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":"2103.15753","title":"Privacy and Trust Redefined in Federated Machine Learning","created":"2021-03-29","cited_by_count":13,"area":null},{"id":"2106.15905","title":"Faithful Edge Federated Learning: Scalability and Privacy","created":"2021-06-30","cited_by_count":13,"area":null},{"id":"2108.06453","title":"Efficient Federated Meta-Learning over Multi-Access Wireless Networks","created":"2021-08-14","cited_by_count":13,"area":null},{"id":"2110.11775","title":"Federated Learning over Wireless IoT Networks with Optimized Communication and Resources","created":"2021-10-22","cited_by_count":13,"area":null},{"id":"2309.14675","title":"FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler","created":"2023-09-26","cited_by_count":13,"area":null},{"id":"2401.04993","title":"AdaFed: Fair Federated Learning via Adaptive Common Descent Direction","created":"2024-01-10","cited_by_count":13,"area":null},{"id":"2502.07059","title":"Federated Continual Learning: Concepts, Challenges, and Solutions","created":"2025-02-10","cited_by_count":13,"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":"2111.07392","title":"Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges","created":"2021-11-14","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":"2202.07391","title":"Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT","created":"2022-02-15","cited_by_count":12,"area":null},{"id":"2210.13111","title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","created":"2022-10-24","cited_by_count":12,"area":null},{"id":"2212.08496","title":"Federated Learning with Flexible Control","created":"2022-12-16","cited_by_count":12,"area":null},{"id":"2403.11460","title":"Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning","created":"2024-03-18","cited_by_count":12,"area":null},{"id":"2405.09839","title":"Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations","created":"2024-05-16","cited_by_count":12,"area":null},{"id":"2409.12626","title":"Green Federated Learning: A new era of Green Aware AI","created":"2024-09-19","cited_by_count":12,"area":null},{"id":"2111.14345","title":"SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning","created":"2021-11-29","cited_by_count":11,"area":null},{"id":"2207.03681","title":"A Survey on Participant Selection for Federated Learning in Mobile Networks","created":"2022-07-08","cited_by_count":11,"area":null},{"id":"2101.03627","title":"Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks","created":"2021-01-10","cited_by_count":10,"area":null},{"id":"2103.02270","title":"Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning","created":"2021-03-03","cited_by_count":10,"area":null},{"id":"2104.13130","title":"Secure and Efficient Federated Learning Through Layering and Sharding Blockchain","created":"2021-04-27","cited_by_count":10,"area":null},{"id":"2105.04153","title":"Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates","created":"2021-05-10","cited_by_count":10,"area":null},{"id":"2106.06796","title":"Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning","created":"2021-06-12","cited_by_count":10,"area":null},{"id":"2107.06548","title":"Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data","created":"2021-07-14","cited_by_count":10,"area":null},{"id":"2204.12426","title":"Time-triggered Federated Learning over Wireless Networks","created":"2022-04-26","cited_by_count":10,"area":null},{"id":"2208.07978","title":"Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion","created":"2022-08-16","cited_by_count":10,"area":null},{"id":"2301.03062","title":"AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices","created":"2023-01-08","cited_by_count":10,"area":null},{"id":"2109.03999","title":"Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2109.06710","title":"Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling","created":"2021-09-14","cited_by_count":9,"area":null},{"id":"2110.14937","title":"Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT","created":"2021-10-28","cited_by_count":9,"area":null},{"id":"2111.11204","title":"Client Selection in Federated Learning based on Gradients Importance","created":"2021-11-19","cited_by_count":9,"area":null},{"id":"2204.03726","title":"Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds","created":"2022-04-07","cited_by_count":9,"area":null}],"next_cursor":"100"}