{"total":43,"rows":[{"id":"2103.00710","title":"Towards Personalized Federated Learning","created":"2021-03-01","cited_by_count":464,"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":"2211.08413","title":"Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges","created":"2022-11-15","cited_by_count":138,"area":null},{"id":"2403.16460","title":"FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data","created":"2024-03-25","cited_by_count":25,"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":"2501.01850","title":"LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data","created":"2025-01-03","cited_by_count":15,"area":null},{"id":"2401.17124","title":"Spectral Co-Distillation for Personalized Federated Learning","created":"2024-01-29","cited_by_count":8,"area":null},{"id":"2311.16535","title":"Contrastive encoder pre-training-based clustered federated learning for heterogeneous data","created":"2023-11-28","cited_by_count":5,"area":null},{"id":"2410.18862","title":"FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning","created":"2024-10-24","cited_by_count":5,"area":null},{"id":"2502.03340","title":"Interaction-Aware Gaussian Weighting for Clustered Federated Learning","created":"2025-02-05","cited_by_count":5,"area":null},{"id":"2410.13602","title":"Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach","created":"2024-10-17","cited_by_count":4,"area":null},{"id":"2210.15827","title":"Federated Learning with Intermediate Representation Regularization","created":"2022-10-28","cited_by_count":3,"area":null},{"id":"2310.05397","title":"Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies","created":"2023-10-09","cited_by_count":3,"area":null},{"id":"2409.18356","title":"FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration","created":"2024-09-27","cited_by_count":2,"area":null},{"id":"2501.13604","title":"FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences","created":"2025-01-23","cited_by_count":2,"area":null},{"id":"2501.17512","title":"A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2503.05474","title":"Personalized Federated Learning via Learning Dynamic Graphs","created":"2025-03-07","cited_by_count":2,"area":null},{"id":"2509.23683","title":"Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2210.01863","title":"Group Personalized Federated Learning","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2306.14483","title":"Medical Federated Model with Mixture of Personalized and Sharing Components","created":"2023-06-26","cited_by_count":1,"area":null},{"id":"2309.14088","title":"REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings","created":"2023-09-25","cited_by_count":1,"area":null},{"id":"2408.09545","title":"Seamless Integration: Sampling Strategies in Federated Learning Systems","created":"2024-08-18","cited_by_count":1,"area":null},{"id":"2410.08934","title":"Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees","created":"2024-10-11","cited_by_count":1,"area":null},{"id":"2507.12903","title":"Federated Learning for Commercial Image Sources","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2403.09284","title":"DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2405.15407","title":"Towards Client Driven Federated Learning","created":"2024-05-24","cited_by_count":0,"area":null},{"id":"2406.07800","title":"Class-Wise Federated Averaging for Efficient Personalization","created":"2024-06-12","cited_by_count":0,"area":null},{"id":"2407.12287","title":"CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2408.06903","title":"Heterogeneity: An Open Challenge for Federated On-board Machine Learning","created":"2024-08-13","cited_by_count":0,"area":null},{"id":"2410.14212","title":"Comparative Evaluation of Clustered Federated Learning Methods","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2503.04231","title":"One-Shot Clustering for Federated Learning","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2509.01587","title":"One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2509.15844","title":"FedHK-MVFC: Federated Heat Kernel Multi-View Clustering","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2510.03380","title":"A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2510.26350","title":"UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federation","created":"2025-10-30","cited_by_count":0,"area":null},{"id":"2603.02785","title":"HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2605.00892","title":"When To Adapt? Adapting the Model or Data in Federated Medical Imaging","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.11165","title":"COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2607.13045","title":"Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2608.04753","title":"Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.07157","title":"Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning","created":"2026-08-07","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},{"id":"2610.03054","title":"RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}