{"total":44,"rows":[{"id":"2306.11867","title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration","created":"2023-06-20","cited_by_count":97,"area":null},{"id":"2307.04722","title":"Advances and Challenges in Meta-Learning: A Technical Review","created":"2023-07-10","cited_by_count":65,"area":null},{"id":"2206.00799","title":"Federated Learning under Distributed Concept Drift","created":"2022-06-01","cited_by_count":36,"area":null},{"id":"2307.07393","title":"L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning","created":"2023-07-14","cited_by_count":30,"area":null},{"id":"2404.02478","title":"FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning","created":"2024-04-03","cited_by_count":27,"area":null},{"id":"2203.12285","title":"Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching","created":"2022-03-23","cited_by_count":24,"area":null},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2312.06290","title":"Exploiting Label Skews in Federated Learning with Model Concatenation","created":"2023-12-11","cited_by_count":14,"area":null},{"id":"2411.00329","title":"Personalized Federated Learning via Feature Distribution Adaptation","created":"2024-11-01","cited_by_count":13,"area":null},{"id":"2210.16656","title":"Auxo: Efficient Federated Learning via Scalable Client Clustering","created":"2022-10-29","cited_by_count":8,"area":null},{"id":"2410.18478","title":"Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift","created":"2024-10-24","cited_by_count":8,"area":null},{"id":"2307.15245","title":"A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design","created":"2023-07-28","cited_by_count":5,"area":null},{"id":"2407.12410","title":"Proximity-based Self-Federated Learning","created":"2024-07-17","cited_by_count":5,"area":null},{"id":"2303.00897","title":"StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation","created":"2023-03-02","cited_by_count":4,"area":null},{"id":"2405.15632","title":"Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning","created":"2024-05-24","cited_by_count":4,"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":"2410.18352","title":"FedBaF: Federated Learning Aggregation Biased by a Foundation Model","created":"2024-10-24","cited_by_count":3,"area":null},{"id":"2411.01580","title":"Federated Learning Clients Clustering with Adaptation to Data Drifts","created":"2024-11-03","cited_by_count":3,"area":null},{"id":"2308.15821","title":"Federated Two Stage Decoupling With Adaptive Personalization Layers","created":"2023-08-30","cited_by_count":2,"area":null},{"id":"2404.13324","title":"Collaborative Visual Place Recognition through Federated Learning","created":"2024-04-20","cited_by_count":2,"area":null},{"id":"2410.07039","title":"Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare","created":"2024-10-09","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":"2404.13515","title":"FedTrans: Efficient Federated Learning via Multi-Model Transformation","created":"2024-04-21","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":"2502.05765","title":"Privacy-Preserving Dataset Combination","created":"2025-02-09","cited_by_count":1,"area":null},{"id":"2506.20285","title":"Distilling A Universal Expert from Clustered Federated Learning","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2506.22427","title":"CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings","created":"2025-06-27","cited_by_count":1,"area":null},{"id":"2303.13015","title":"Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks","created":"2023-03-23","cited_by_count":0,"area":null},{"id":"2308.13158","title":"DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT","created":"2023-08-25","cited_by_count":0,"area":null},{"id":"2312.15191","title":"Personalized Federated Learning with Contextual Modulation and Meta-Learning","created":"2023-12-23","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":"2405.15644","title":"Harnessing Increased Client Participation with Cohort-Parallel Federated Learning","created":"2024-05-24","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":"2410.14212","title":"Comparative Evaluation of Clustered Federated Learning Methods","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2503.08652","title":"Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition","created":"2025-03-11","cited_by_count":0,"area":null},{"id":"2505.02540","title":"Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data","created":"2025-05-05","cited_by_count":0,"area":null},{"id":"2506.00932","title":"Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity","created":"2025-06-01","cited_by_count":0,"area":null},{"id":"2506.07769","title":"Clustered Federated Learning via Embedding Distributions","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2506.20413","title":"Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2602.18658","title":"Communication-Efficient Personalized Adaptation via Federated-Local Model Merging","created":"2026-02-20","cited_by_count":0,"area":null},{"id":"2602.23504","title":"FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.11620","title":"Personalized Federated Learning via Gaussian Generative Modeling","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2606.10595","title":"From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2607.28338","title":"Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata","created":"2026-07-30","cited_by_count":0,"area":null}],"next_cursor":null}