{"total":63,"rows":[{"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":"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":"2207.07223","title":"Accelerated Federated Learning with Decoupled Adaptive Optimization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2105.08982","title":"Prototype Guided Federated Learning of Visual Feature Representations","created":"2021-05-19","cited_by_count":22,"area":null},{"id":"2502.19980","title":"Can Textual Gradient Work in Federated Learning?","created":"2025-02-27","cited_by_count":15,"area":null},{"id":"2411.18607","title":"Task Arithmetic Through The Lens Of One-Shot Federated Learning","created":"2024-11-27","cited_by_count":12,"area":null},{"id":"2105.02501","title":"Federated Face Recognition","created":"2021-05-06","cited_by_count":9,"area":null},{"id":"2301.09109","title":"Federated Recommendation with Additive Personalization","created":"2023-01-22","cited_by_count":9,"area":null},{"id":"2409.19560","title":"Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving","created":"2024-09-29","cited_by_count":8,"area":null},{"id":"2208.02442","title":"FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2506.12213","title":"Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation","created":"2025-06-13","cited_by_count":7,"area":null},{"id":"2207.10751","title":"Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization","created":"2022-07-21","cited_by_count":6,"area":null},{"id":"2210.02614","title":"Federated Learning with Server Learning: Enhancing Performance for Non-IID Data","created":"2022-10-06","cited_by_count":6,"area":null},{"id":"2302.10413","title":"CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization","created":"2023-02-21","cited_by_count":5,"area":null},{"id":"2311.08202","title":"Federated Skewed Label Learning with Logits Fusion","created":"2023-11-14","cited_by_count":5,"area":null},{"id":"2503.17070","title":"A Thorough Assessment of the Non-IID Data Impact in Federated Learning","created":"2025-03-21","cited_by_count":5,"area":null},{"id":"2212.02304","title":"Matching DNN Compression and Cooperative Training with Resources and Data Availability","created":"2022-12-02","cited_by_count":4,"area":null},{"id":"2203.12281","title":"Efficient Fully Distributed Federated Learning with Adaptive Local Links","created":"2022-03-23","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":"2310.05269","title":"Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications","created":"2023-10-08","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":"2404.15919","title":"An Element-Wise Weights Aggregation Method for Federated Learning","created":"2024-04-24","cited_by_count":3,"area":null},{"id":"2404.18519","title":"On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks","created":"2024-04-29","cited_by_count":3,"area":null},{"id":"2411.01240","title":"Optimizing Federated Learning by Entropy-Based Client Selection","created":"2024-11-02","cited_by_count":3,"area":null},{"id":"2211.07248","title":"FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity","created":"2022-11-14","cited_by_count":2,"area":null},{"id":"2302.07684","title":"A Federated Learning Benchmark for Drug-Target Interaction","created":"2023-02-15","cited_by_count":2,"area":null},{"id":"2311.10002","title":"Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network","created":"2023-11-16","cited_by_count":2,"area":null},{"id":"2402.04409","title":"Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2403.16372","title":"SignSGD with Federated Voting","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2410.08734","title":"Gradients Stand-in for Defending Deep Leakage in Federated Learning","created":"2024-10-11","cited_by_count":2,"area":null},{"id":"2204.13414","title":"Improving the Robustness of Federated Learning for Severely Imbalanced Datasets","created":"2022-04-28","cited_by_count":1,"area":null},{"id":"2305.16351","title":"Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2306.03401","title":"A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging","created":"2023-06-06","cited_by_count":1,"area":null},{"id":"2310.08961","title":"PAGE: Equilibrate Personalization and Generalization in Federated Learning","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2402.01340","title":"SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding","created":"2024-02-02","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":"2404.12023","title":"Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemes","created":"2024-04-18","cited_by_count":1,"area":null},{"id":"2405.09276","title":"Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments","created":"2024-05-15","cited_by_count":1,"area":null},{"id":"2406.12564","title":"Low-Resource Machine Translation through the Lens of Personalized Federated Learning","created":"2024-06-18","cited_by_count":1,"area":null},{"id":"2503.04052","title":"The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2505.23973","title":"Adaptive Deadline and Batch Layered Synchronized Federated Learning","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2506.23583","title":"Detect \\& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning","created":"2025-06-30","cited_by_count":1,"area":null},{"id":"2509.19921","title":"On the Fragility of Contribution Score Computation in Federated Learning","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2511.11778","title":"CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments","created":"2025-11-14","cited_by_count":1,"area":null},{"id":"2602.21721","title":"Private and Robust Contribution Evaluation in Federated Learning","created":"2026-02-25","cited_by_count":1,"area":null},{"id":"2212.12191","title":"Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments","created":"2022-12-23","cited_by_count":0,"area":null},{"id":"2301.10400","title":"When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning","created":"2023-01-25","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":"2402.14346","title":"Dependable Distributed Training of Compressed Machine Learning Models","created":"2024-02-22","cited_by_count":0,"area":null},{"id":"2407.08337","title":"FedLog: Personalized Federated Classification with Less Communication and More Flexibility","created":"2024-07-11","cited_by_count":0,"area":null},{"id":"2503.06158","title":"Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty","created":"2025-03-08","cited_by_count":0,"area":null},{"id":"2505.05486","title":"FedAvgen: Metadata for Model Aggregation In Communication Systems","created":"2025-04-28","cited_by_count":0,"area":null},{"id":"2505.19823","title":"LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2507.11471","title":"D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2509.25560","title":"Lightweight and Robust Federated Data Valuation","created":"2025-09-29","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":"2602.20450","title":"Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2605.00458","title":"Federated Learning with Hypergradient-based Online Update of Aggregation Weights","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.08616","title":"Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning","created":"2026-05-09","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":"2608.14654","title":"FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.25794","title":"Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.05223","title":"FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification","created":"2026-09-04","cited_by_count":0,"area":null}],"next_cursor":null}