{"total":51,"rows":[{"id":"2105.10056","title":"Data-Free Knowledge Distillation for Heterogeneous Federated Learning","created":"2021-05-20","cited_by_count":383,"area":"distill"},{"id":"2108.13323","title":"FedKD: Communication Efficient Federated Learning via Knowledge Distillation","created":"2021-08-30","cited_by_count":187,"area":"distill"},{"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":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2102.02514","title":"FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning","created":"2021-02-04","cited_by_count":69,"area":null},{"id":"2107.11621","title":"FedLab: A Flexible Federated Learning Framework","created":"2021-07-24","cited_by_count":55,"area":null},{"id":"2302.02069","title":"Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning","created":"2023-02-04","cited_by_count":40,"area":"safety"},{"id":"2207.06936","title":"Multi-Level Branched Regularization for Federated Learning","created":"2022-07-14","cited_by_count":34,"area":null},{"id":"2107.00051","title":"Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data","created":"2021-06-30","cited_by_count":31,"area":"distill"},{"id":"2109.03775","title":"FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models","created":"2021-09-08","cited_by_count":31,"area":null},{"id":"2110.15210","title":"Towards Model Agnostic Federated Learning Using Knowledge Distillation","created":"2021-10-28","cited_by_count":31,"area":"distill"},{"id":"2211.04742","title":"Knowledge Distillation for Federated Learning: a Practical Guide","created":"2022-11-09","cited_by_count":31,"area":"distill"},{"id":"2209.14851","title":"Meta Knowledge Condensation for Federated Learning","created":"2022-09-29","cited_by_count":26,"area":null},{"id":"2304.01731","title":"Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher","created":"2023-04-04","cited_by_count":25,"area":"distill"},{"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":"2108.05681","title":"Semantics-Native Communication with Contextual Reasoning","created":"2021-08-12","cited_by_count":22,"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":"2109.07258","title":"Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting","created":"2021-09-15","cited_by_count":19,"area":null},{"id":"2110.03681","title":"Neural Tangent Kernel Empowered Federated Learning","created":"2021-10-07","cited_by_count":15,"area":null},{"id":"2404.08564","title":"Federated Distillation: A Survey","created":"2024-04-02","cited_by_count":15,"area":null},{"id":"2201.00011","title":"An Efficient Federated Distillation Learning System for Multi-task Time Series Classification","created":"2021-12-30","cited_by_count":14,"area":null},{"id":"2110.09910","title":"FedHe: Heterogeneous Models and Communication-Efficient Federated Learning","created":"2021-10-19","cited_by_count":13,"area":null},{"id":"2205.01423","title":"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2303.07113","title":"FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection","created":"2023-03-10","cited_by_count":12,"area":"distill"},{"id":"2208.07978","title":"Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion","created":"2022-08-16","cited_by_count":10,"area":null},{"id":"2205.00706","title":"FedDKD: Federated Learning with Decentralized Knowledge Distillation","created":"2022-05-02","cited_by_count":9,"area":"distill"},{"id":"2306.02368","title":"Revisiting Data-Free Knowledge Distillation with Poisoned Teachers","created":"2023-06-04","cited_by_count":9,"area":"safety"},{"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":"2407.07557","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","created":"2024-07-10","cited_by_count":8,"area":null},{"id":"2308.07761","title":"NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients","created":"2023-08-15","cited_by_count":7,"area":null},{"id":"2205.11440","title":"Federated Distillation based Indoor Localization for IoT Networks","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2110.09674","title":"Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation","created":"2021-10-19","cited_by_count":4,"area":"distill"},{"id":"2308.10162","title":"Rethinking Client Drift in Federated Learning: A Logit Perspective","created":"2023-08-20","cited_by_count":4,"area":null},{"id":"2504.14694","title":"Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data","created":"2025-04-20","cited_by_count":4,"area":"distill"},{"id":"2106.14265","title":"Reward-Based 1-bit Compressed Federated Distillation on Blockchain","created":"2021-06-27","cited_by_count":2,"area":"distill"},{"id":"2411.10071","title":"Evidential Federated Learning for Skin Lesion Image Classification","created":"2024-11-15","cited_by_count":2,"area":"distill"},{"id":"2505.20456","title":"Federated Learning-Distillation Alternation for Resource-Constrained IoT","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2108.05670","title":"Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates","created":"2021-08-12","cited_by_count":1,"area":null},{"id":"2312.16799","title":"Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications","created":"2023-12-28","cited_by_count":1,"area":"distill"},{"id":"2312.17430","title":"LEFL: Low Entropy Client Sampling in Federated Learning","created":"2023-12-29","cited_by_count":1,"area":null},{"id":"2506.20431","title":"Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation","created":"2025-06-25","cited_by_count":1,"area":"distill"},{"id":"2509.15147","title":"Who to Trust? Aggregating Client Predictions in Federated Distillation","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2604.10678","title":"FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation","created":"2026-04-12","cited_by_count":1,"area":null},{"id":"2112.03267","title":"Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding","created":"2021-12-05","cited_by_count":0,"area":null},{"id":"2205.02438","title":"Uncertainty Minimization for Personalized Federated Semi-Supervised Learning","created":"2022-05-05","cited_by_count":0,"area":null},{"id":"2301.03720","title":"Federated Learning for Energy Constrained IoT devices: A systematic mapping study","created":"2023-01-09","cited_by_count":0,"area":null},{"id":"2310.02549","title":"Heterogeneous Federated Learning Using Knowledge Codistillation","created":"2023-10-04","cited_by_count":0,"area":"distill"},{"id":"2409.05359","title":"FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data","created":"2024-09-09","cited_by_count":0,"area":"distill"},{"id":"2412.18460","title":"GeFL: Model-Agnostic Federated Learning with Generative Models","created":"2024-12-24","cited_by_count":0,"area":null},{"id":"2602.14301","title":"DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices","created":"2026-02-15","cited_by_count":0,"area":"moe"},{"id":"2602.18749","title":"Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation","created":"2026-02-21","cited_by_count":0,"area":"lr"}],"next_cursor":null}