{"total":181,"rows":[{"id":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99,"area":null},{"id":"2302.13485","title":"FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning","created":"2023-02-27","cited_by_count":70,"area":null},{"id":"2305.19229","title":"FedDisco: Federated Learning with Discrepancy-Aware Collaboration","created":"2023-05-30","cited_by_count":67,"area":null},{"id":"2302.10429","title":"FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy","created":"2023-02-21","cited_by_count":49,"area":null},{"id":"2401.11755","title":"FedGTA: Topology-aware Averaging for Federated Graph Learning","created":"2024-01-22","cited_by_count":44,"area":null},{"id":"2301.09152","title":"Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data","created":"2023-01-22","cited_by_count":40,"area":null},{"id":"2311.14975","title":"Eliminating Domain Bias for Federated Learning in Representation Space","created":"2023-11-25","cited_by_count":38,"area":null},{"id":"2308.03457","title":"Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data","created":"2023-08-07","cited_by_count":35,"area":null},{"id":"2211.08025","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","created":"2022-11-15","cited_by_count":33,"area":null},{"id":"2404.18962","title":"An Aggregation-Free Federated Learning for Tackling Data Heterogeneity","created":"2024-04-29","cited_by_count":33,"area":null},{"id":"2404.14061","title":"FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning","created":"2024-04-22","cited_by_count":27,"area":"distill"},{"id":"2306.05706","title":"Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization","created":"2023-06-09","cited_by_count":21,"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":"2405.03409","title":"LightTR: A Lightweight Framework for Federated Trajectory Recovery","created":"2024-05-06","cited_by_count":19,"area":null},{"id":"2503.15842","title":"FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors","created":"2025-03-20","cited_by_count":19,"area":null},{"id":"2303.08678","title":"Visual Prompt Based Personalized Federated Learning","created":"2023-03-15","cited_by_count":16,"area":null},{"id":"2408.16288","title":"OpenFGL: A Comprehensive Benchmark for Federated Graph Learning","created":"2024-08-29","cited_by_count":16,"area":null},{"id":"2303.12317","title":"Re-thinking Federated Active Learning based on Inter-class Diversity","created":"2023-03-22","cited_by_count":14,"area":null},{"id":"2404.09816","title":"FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity","created":"2024-04-15","cited_by_count":13,"area":null},{"id":"2411.00329","title":"Personalized Federated Learning via Feature Distribution Adaptation","created":"2024-11-01","cited_by_count":13,"area":null},{"id":"2206.00686","title":"Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data","created":"2022-06-01","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":"2503.06457","title":"Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning","created":"2025-03-09","cited_by_count":12,"area":null},{"id":"2601.22416","title":"MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning","created":"2026-01-29","cited_by_count":11,"area":null},{"id":"2311.06141","title":"Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification","created":"2023-11-10","cited_by_count":10,"area":null},{"id":"2403.17287","title":"Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study","created":"2024-03-26","cited_by_count":10,"area":null},{"id":"2404.08447","title":"Federated Optimization with Doubly Regularized Drift Correction","created":"2024-04-12","cited_by_count":10,"area":null},{"id":"2212.02191","title":"On the effectiveness of partial variance reduction in federated learning with heterogeneous data","created":"2022-12-05","cited_by_count":9,"area":null},{"id":"2307.05358","title":"Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators","created":"2023-07-11","cited_by_count":9,"area":null},{"id":"2310.05143","title":"ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning","created":"2023-10-08","cited_by_count":9,"area":null},{"id":"2306.00792","title":"Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning","created":"2023-06-01","cited_by_count":8,"area":null},{"id":"2310.15165","title":"Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition","created":"2023-10-23","cited_by_count":8,"area":null},{"id":"2403.01387","title":"A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications","created":"2024-03-03","cited_by_count":8,"area":null},{"id":"2404.12130","title":"One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity","created":"2024-04-18","cited_by_count":8,"area":null},{"id":"2406.01960","title":"Certifiably Byzantine-Robust Federated Conformal Prediction","created":"2024-06-04","cited_by_count":8,"area":null},{"id":"2412.03752","title":"Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning","created":"2024-12-04","cited_by_count":8,"area":null},{"id":"2303.13033","title":"Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging","created":"2023-03-23","cited_by_count":7,"area":null},{"id":"2308.06051","title":"Towards Instance-adaptive Inference for Federated Learning","created":"2023-08-11","cited_by_count":7,"area":null},{"id":"2312.09881","title":"Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes","created":"2023-12-15","cited_by_count":7,"area":null},{"id":"2407.07084","title":"Stabilized Proximal-Point Methods for Federated Optimization","created":"2024-07-09","cited_by_count":7,"area":null},{"id":"2502.07620","title":"Resilient Contrastive Pre-training under Non-Stationary Drift","created":"2025-02-11","cited_by_count":7,"area":null},{"id":"2503.06426","title":"Federated Learning for Diffusion Models","created":"2025-03-09","cited_by_count":7,"area":"diffusion"},{"id":"2503.15111","title":"FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking","created":"2025-03-19","cited_by_count":7,"area":null},{"id":"2603.05116","title":"FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning","created":"2026-03-05","cited_by_count":7,"area":null},{"id":"2311.13163","title":"Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning","created":"2023-11-22","cited_by_count":6,"area":null},{"id":"2406.02447","title":"Federated Class-Incremental Learning with Hierarchical Generative Prototypes","created":"2024-06-04","cited_by_count":6,"area":null},{"id":"2501.11360","title":"Federated Learning with Sample-level Client Drift Mitigation","created":"2025-01-20","cited_by_count":6,"area":null},{"id":"2504.05238","title":"Federated Learning for Medical Image Classification: A Comprehensive Benchmark","created":"2025-04-07","cited_by_count":6,"area":null},{"id":"2404.09259","title":"FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity","created":"2024-04-14","cited_by_count":5,"area":null},{"id":"2405.00955","title":"Recovering Labels from Local Updates in Federated Learning","created":"2024-05-02","cited_by_count":5,"area":null},{"id":"2405.15405","title":"Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification","created":"2024-05-24","cited_by_count":5,"area":null},{"id":"2308.10162","title":"Rethinking Client Drift in Federated Learning: A Logit Perspective","created":"2023-08-20","cited_by_count":4,"area":null},{"id":"2308.11953","title":"When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation","created":"2023-08-23","cited_by_count":4,"area":null},{"id":"2405.18291","title":"FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2405.18983","title":"Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping","created":"2024-05-29","cited_by_count":4,"area":null},{"id":"2412.18904","title":"FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning","created":"2024-12-25","cited_by_count":4,"area":null},{"id":"2504.11903","title":"FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data","created":"2025-04-16","cited_by_count":4,"area":null},{"id":"2211.08914","title":"Dual Class-Aware Contrastive Federated Semi-Supervised Learning","created":"2022-11-16","cited_by_count":3,"area":null},{"id":"2311.04944","title":"Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2312.07165","title":"Language-Guided Transformer for Federated Multi-Label Classification","created":"2023-12-12","cited_by_count":3,"area":null},{"id":"2402.16255","title":"Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models","created":"2024-02-26","cited_by_count":3,"area":null},{"id":"2403.03333","title":"Federated Learning over Connected Modes","created":"2024-03-05","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":"2405.10618","title":"Distributed Event-Based Learning via ADMM","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2405.13879","title":"FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2405.17267","title":"FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation","created":"2024-05-27","cited_by_count":3,"area":"distill"},{"id":"2405.17457","title":"Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory","created":"2024-05-22","cited_by_count":3,"area":"diffusion"},{"id":"2406.15925","title":"Federated Adversarial Learning for Robust Autonomous Landing Runway Detection","created":"2024-06-22","cited_by_count":3,"area":null},{"id":"2412.01630","title":"Review of Mathematical Optimization in Federated Learning","created":"2024-12-02","cited_by_count":3,"area":null},{"id":"2504.18020","title":"Federated Client-tailored Adapter for Medical Image Segmentation","created":"2025-04-25","cited_by_count":3,"area":null},{"id":"2505.08325","title":"FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing","created":"2025-05-13","cited_by_count":3,"area":null},{"id":"2506.21012","title":"FedSC: Federated Learning with Semantic-Aware Collaboration","created":"2025-06-26","cited_by_count":3,"area":null},{"id":"2507.06482","title":"FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning","created":"2025-07-09","cited_by_count":3,"area":"diffusion"},{"id":"2207.08391","title":"Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update","created":"2022-07-18","cited_by_count":2,"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":"2302.06089","title":"Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading","created":"2023-02-13","cited_by_count":2,"area":null},{"id":"2308.03901","title":"FLIPS: Federated Learning using Intelligent Participant Selection","created":"2023-08-07","cited_by_count":2,"area":null},{"id":"2311.12358","title":"Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence","created":"2023-11-21","cited_by_count":2,"area":null},{"id":"2403.11041","title":"FAGH: Accelerating Federated Learning with Approximated Global Hessian","created":"2024-03-16","cited_by_count":2,"area":null},{"id":"2412.11660","title":"Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning","created":"2024-12-16","cited_by_count":2,"area":null},{"id":"2501.07378","title":"FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation","created":"2025-01-13","cited_by_count":2,"area":null},{"id":"2504.02114","title":"On Model Protection in Federated Learning against Eavesdropping Attacks","created":"2025-04-02","cited_by_count":2,"area":null},{"id":"2504.14188","title":"Rethinking Client-oriented Federated Graph Learning","created":"2025-04-19","cited_by_count":2,"area":null},{"id":"2505.19699","title":"Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments","created":"2025-05-26","cited_by_count":2,"area":"moe"},{"id":"2507.08617","title":"Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift","created":"2025-07-11","cited_by_count":2,"area":"distill"},{"id":"2509.19674","title":"C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2511.12044","title":"FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification","created":"2025-11-15","cited_by_count":2,"area":null},{"id":"2512.07208","title":"Geometric Prior-Guided Federated Prompt Calibration","created":"2025-12-08","cited_by_count":2,"area":null},{"id":"2207.09657","title":"Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology","created":"2022-07-20","cited_by_count":1,"area":null},{"id":"2301.12798","title":"Reliable Federated Disentangling Network for Non-IID Domain Feature","created":"2023-01-30","cited_by_count":1,"area":null},{"id":"2307.13716","title":"FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning","created":"2023-07-25","cited_by_count":1,"area":null},{"id":"2311.04818","title":"Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment","created":"2023-11-08","cited_by_count":1,"area":null},{"id":"2311.06921","title":"Concept Matching: Clustering-based Federated Continual Learning","created":"2023-11-12","cited_by_count":1,"area":null},{"id":"2405.20046","title":"Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2407.03862","title":"FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning","created":"2024-07-04","cited_by_count":1,"area":null},{"id":"2410.12316","title":"TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic","created":"2024-10-16","cited_by_count":1,"area":null},{"id":"2503.10262","title":"A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification","created":"2025-03-13","cited_by_count":1,"area":null},{"id":"2504.03505","title":"Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2504.09493","title":"Federated Prototype Graph Learning","created":"2025-04-13","cited_by_count":1,"area":null},{"id":"2505.09287","title":"Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features","created":"2025-05-14","cited_by_count":1,"area":null}],"next_cursor":"100"}