{"total":61,"rows":[{"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":"2111.00465","title":"DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning","created":"2021-10-31","cited_by_count":41,"area":null},{"id":"2101.00787","title":"Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation","created":"2021-01-04","cited_by_count":40,"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":"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":"2206.05187","title":"On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond","created":"2022-06-10","cited_by_count":38,"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":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"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":"2108.00365","title":"A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee","created":"2021-08-01","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":"2111.10763","title":"Decentralized Unsupervised Learning of Visual Representations","created":"2021-11-21","cited_by_count":15,"area":null},{"id":"2210.15496","title":"Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles","created":"2022-10-27","cited_by_count":15,"area":null},{"id":"2302.03662","title":"Federated Learning with Regularized Client Participation","created":"2023-02-07","cited_by_count":14,"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":"2210.13111","title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","created":"2022-10-24","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":"2204.03726","title":"Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds","created":"2022-04-07","cited_by_count":9,"area":null},{"id":"2404.13804","title":"Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2209.00195","title":"To Store or Not? Online Data Selection for Federated Learning with Limited Storage","created":"2022-09-01","cited_by_count":7,"area":null},{"id":"2210.15025","title":"Addressing Heterogeneity in Federated Learning via Distributional Transformation","created":"2022-10-26","cited_by_count":6,"area":null},{"id":"2411.07523","title":"Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective","created":"2024-11-12","cited_by_count":5,"area":null},{"id":"2212.05492","title":"Client Selection for Federated Bayesian Learning","created":"2022-12-11","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":"2405.20431","title":"Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective","created":"2024-05-30","cited_by_count":4,"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":"2303.14966","title":"Adaptive Federated Learning via New Entropy Approach","created":"2023-03-27","cited_by_count":3,"area":null},{"id":"2306.01339","title":"Resource-Efficient Federated Hyperdimensional Computing","created":"2023-06-02","cited_by_count":3,"area":null},{"id":"2311.09270","title":"FedCode: Communication-Efficient Federated Learning via Transferring Codebooks","created":"2023-11-15","cited_by_count":3,"area":null},{"id":"2405.04875","title":"SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments","created":"2024-05-08","cited_by_count":3,"area":null},{"id":"2112.11485","title":"On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge","created":"2021-12-21","cited_by_count":2,"area":null},{"id":"2203.12738","title":"Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing","created":"2022-03-23","cited_by_count":2,"area":null},{"id":"2309.17097","title":"Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation","created":"2023-09-29","cited_by_count":2,"area":null},{"id":"2403.02347","title":"On the Convergence of Federated Learning Algorithms without Data Similarity","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2505.11570","title":"Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2202.11349","title":"Efficient Distributed DNNs in the Mobile-edge-cloud Continuum","created":"2022-02-23","cited_by_count":1,"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":"2207.00041","title":"DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring","created":"2022-06-30","cited_by_count":1,"area":null},{"id":"2311.14401","title":"Prototype of deployment of Federated Learning with IoT devices","created":"2023-11-24","cited_by_count":1,"area":null},{"id":"2312.02102","title":"Mitigating Data Injection Attacks on Federated Learning","created":"2023-12-04","cited_by_count":1,"area":null},{"id":"2402.05050","title":"Federated Learning Can Find Friends That Are Advantageous","created":"2024-02-07","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":"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":"2504.01921","title":"Client Selection in Federated Learning with Data Heterogeneity and Network Latencies","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2210.03444","title":"Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent","created":"2022-10-07","cited_by_count":0,"area":null},{"id":"2211.12640","title":"Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering","created":"2022-11-23","cited_by_count":0,"area":null},{"id":"2308.09082","title":"Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2310.04515","title":"Utilizing Free Clients in Federated Learning for Focused Model Enhancement","created":"2023-10-06","cited_by_count":0,"area":null},{"id":"2310.09444","title":"Tackling Heterogeneity in Medical Federated learning via Vision Transformers","created":"2023-10-13","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":"2410.10833","title":"Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2502.09303","title":"Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology","created":"2025-02-13","cited_by_count":0,"area":null},{"id":"2503.12435","title":"XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing","created":"2025-03-16","cited_by_count":0,"area":null},{"id":"2506.15923","title":"PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2508.14565","title":"Cooperative SGD with Dynamic Mixing Matrices","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2509.03709","title":"From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2510.07766","title":"FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2604.10662","title":"Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2609.08312","title":"Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.14246","title":"Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data","created":"2026-09-13","cited_by_count":0,"area":null}],"next_cursor":null}