{"total":101,"rows":[{"id":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2303.15991","title":"Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks","created":"2023-03-26","cited_by_count":95,"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":"2107.10996","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","created":"2021-07-23","cited_by_count":40,"area":null},{"id":"2311.01483","title":"FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks","created":"2023-11-02","cited_by_count":38,"area":null},{"id":"2106.09397","title":"Quantized Federated Learning under Transmission Delay and Outage Constraints","created":"2021-06-17","cited_by_count":37,"area":null},{"id":"2106.00490","title":"Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints","created":"2021-05-31","cited_by_count":36,"area":null},{"id":"2308.13894","title":"FwdLLM: Efficient FedLLM using Forward Gradient","created":"2023-08-26","cited_by_count":26,"area":null},{"id":"2205.10162","title":"FedAdapter: Efficient Federated Learning for Modern NLP","created":"2022-05-20","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":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"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":"2103.01422","title":"Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning","created":"2021-03-02","cited_by_count":15,"area":null},{"id":"2206.02398","title":"Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks","created":"2022-06-06","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":"2108.06453","title":"Efficient Federated Meta-Learning over Multi-Access Wireless Networks","created":"2021-08-14","cited_by_count":13,"area":null},{"id":"2402.05968","title":"Federated Learning Priorities Under the European Union Artificial Intelligence Act","created":"2024-02-05","cited_by_count":13,"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":"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":"2204.12426","title":"Time-triggered Federated Learning over Wireless Networks","created":"2022-04-26","cited_by_count":10,"area":null},{"id":"2306.12212","title":"MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates","created":"2023-06-21","cited_by_count":10,"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":"2101.02198","title":"Federated Learning over Noisy Channels: Convergence Analysis and Design Examples","created":"2021-01-06","cited_by_count":8,"area":null},{"id":"2204.09746","title":"Efficient Wireless Federated Learning with Partial Model Aggregation","created":"2022-04-20","cited_by_count":8,"area":null},{"id":"2303.10373","title":"Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach","created":"2023-03-18","cited_by_count":8,"area":null},{"id":"2306.05764","title":"Fair yet Asymptotically Equal Collaborative Learning","created":"2023-06-09","cited_by_count":8,"area":null},{"id":"2106.10734","title":"Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning","created":"2021-06-20","cited_by_count":7,"area":null},{"id":"2108.08768","title":"Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks","created":"2021-08-16","cited_by_count":7,"area":null},{"id":"2211.02906","title":"ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme","created":"2022-11-05","cited_by_count":7,"area":null},{"id":"2211.04132","title":"Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design","created":"2022-11-08","cited_by_count":6,"area":null},{"id":"2401.00403","title":"Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection","created":"2023-12-31","cited_by_count":6,"area":null},{"id":"2402.02506","title":"Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things","created":"2024-02-04","cited_by_count":6,"area":null},{"id":"2405.12046","title":"Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach","created":"2024-05-20","cited_by_count":6,"area":null},{"id":"2505.04873","title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","created":"2025-05-08","cited_by_count":6,"area":null},{"id":"2205.14271","title":"Towards Communication-Learning Trade-off for Federated Learning at the Network Edge","created":"2022-05-27","cited_by_count":5,"area":null},{"id":"2209.14900","title":"Joint Optimization of Energy Consumption and Completion Time in Federated Learning","created":"2022-09-29","cited_by_count":5,"area":null},{"id":"2212.00192","title":"Towards Practical Few-shot Federated NLP","created":"2022-12-01","cited_by_count":5,"area":null},{"id":"2302.06730","title":"Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation","created":"2023-02-13","cited_by_count":5,"area":null},{"id":"2305.11654","title":"V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection","created":"2023-05-19","cited_by_count":5,"area":null},{"id":"2104.06990","title":"Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges","created":"2021-04-14","cited_by_count":4,"area":null},{"id":"2204.00489","title":"Accelerating Federated Edge Learning via Topology Optimization","created":"2022-04-01","cited_by_count":4,"area":null},{"id":"2302.00106","title":"Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2311.16741","title":"Asynchronous Wireless Federated Learning with Probabilistic Client Selection","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2405.10513","title":"Federated Learning With Energy Harvesting Devices: An MDP Framework","created":"2024-05-17","cited_by_count":4,"area":null},{"id":"2201.05762","title":"Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection","created":"2022-01-15","cited_by_count":3,"area":null},{"id":"2302.07305","title":"FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks","created":"2023-02-14","cited_by_count":3,"area":null},{"id":"2305.01238","title":"Dynamic Scheduling for Federated Edge Learning with Streaming Data","created":"2023-05-02","cited_by_count":3,"area":null},{"id":"2305.18646","title":"Deep Equilibrium Models Meet Federated Learning","created":"2023-05-29","cited_by_count":3,"area":null},{"id":"2306.04862","title":"A Systematic Literature Review on Client Selection in Federated Learning","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2306.05172","title":"FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2308.03521","title":"Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity","created":"2023-08-04","cited_by_count":3,"area":null},{"id":"2501.01828","title":"Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning","created":"2025-01-03","cited_by_count":3,"area":null},{"id":"2501.09822","title":"pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data","created":"2025-01-16","cited_by_count":3,"area":null},{"id":"2208.04322","title":"Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets","created":"2022-08-08","cited_by_count":2,"area":null},{"id":"2305.01154","title":"FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer","created":"2023-05-02","cited_by_count":2,"area":null},{"id":"2308.16835","title":"FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout","created":"2023-08-31","cited_by_count":2,"area":null},{"id":"2405.03248","title":"Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2501.11267","title":"Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks","created":"2025-01-20","cited_by_count":2,"area":null},{"id":"2504.12181","title":"Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning","created":"2025-04-16","cited_by_count":2,"area":null},{"id":"2506.07581","title":"FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2507.15601","title":"Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity","created":"2025-07-21","cited_by_count":2,"area":null},{"id":"2303.17885","title":"Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis","created":"2023-03-31","cited_by_count":1,"area":null},{"id":"2306.08280","title":"Differentially Private Wireless Federated Learning Using Orthogonal Sequences","created":"2023-06-14","cited_by_count":1,"area":null},{"id":"2307.09977","title":"Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks","created":"2023-07-19","cited_by_count":1,"area":null},{"id":"2309.09253","title":"User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks","created":"2023-09-17","cited_by_count":1,"area":null},{"id":"2311.16021","title":"Scheduling and Communication Schemes for Decentralized Federated Learning","created":"2023-11-27","cited_by_count":1,"area":null},{"id":"2404.14811","title":"FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks","created":"2024-04-23","cited_by_count":1,"area":null},{"id":"2405.00395","title":"Trust Driven On-Demand Scheme for Client Deployment in Federated Learning","created":"2024-05-01","cited_by_count":1,"area":null},{"id":"2410.05662","title":"Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2502.21012","title":"FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2503.13173","title":"PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning","created":"2025-03-17","cited_by_count":1,"area":null},{"id":"2504.14667","title":"Efficient Split Federated Learning for Large Language Models over Communication Networks","created":"2025-04-20","cited_by_count":1,"area":null},{"id":"2507.20871","title":"FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View","created":"2025-07-28","cited_by_count":1,"area":null},{"id":"2210.09881","title":"Random Orthogonalization for Federated Learning in Massive MIMO Systems","created":"2022-10-18","cited_by_count":0,"area":null},{"id":"2303.15799","title":"FedAgg: Adaptive Federated Learning with Aggregated Gradients","created":"2023-03-28","cited_by_count":0,"area":null},{"id":"2304.14663","title":"Client Recruitment for Federated Learning in ICU Length of Stay Prediction","created":"2023-04-28","cited_by_count":0,"area":null},{"id":"2308.09072","title":"Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2309.13643","title":"REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices","created":"2023-09-24","cited_by_count":0,"area":null},{"id":"2311.06756","title":"Personalized Federated Learning via ADMM with Moreau Envelope","created":"2023-11-12","cited_by_count":0,"area":null},{"id":"2312.12417","title":"Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning","created":"2023-12-15","cited_by_count":0,"area":null},{"id":"2401.03159","title":"Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles","created":"2024-01-06","cited_by_count":0,"area":null},{"id":"2402.08769","title":"FLASH: Federated Learning Across Simultaneous Heterogeneities","created":"2024-02-13","cited_by_count":0,"area":null},{"id":"2403.17833","title":"GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning","created":"2024-03-26","cited_by_count":0,"area":null},{"id":"2404.02779","title":"Federated Computing -- Survey on Building Blocks, Extensions and Systems","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2405.06993","title":"Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations","created":"2024-05-11","cited_by_count":0,"area":null},{"id":"2405.15788","title":"Towards Fairness in Provably Communication-Efficient Federated Recommender Systems","created":"2024-05-03","cited_by_count":0,"area":null},{"id":"2410.20659","title":"A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2503.09144","title":"Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2504.01752","title":"A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2506.00743","title":"Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection","created":"2025-05-31","cited_by_count":0,"area":null},{"id":"2507.09546","title":"Lightweight Federated Learning over Wireless Edge Networks","created":"2025-07-13","cited_by_count":0,"area":null},{"id":"2508.02534","title":"Communication and Computation Efficient Split Federated Learning in O-RAN","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2508.13803","title":"Communication-Efficient Federated Learning with Adaptive Number of Participants","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2602.00718","title":"Federated Learning at the Forefront of Fairness: A Multifaceted Perspective","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2605.23984","title":"Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.30892","title":"Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.07702","title":"EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2607.02636","title":"Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2609.23843","title":"Adaptive Determinantal Client Scheduling in Federated Learning","created":"2026-09-20","cited_by_count":0,"area":null}],"next_cursor":"100"}