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Publication search results
found 173 matches
- 2024
- Pengcheng Xiang, Ling Zhou, Lu Tang:
Transfer learning via random forests: A one-shot federated approach. Comput. Stat. Data Anal. 197: 107975 (2024) - Halil Yigit Öksüz
, Fabio Molinari
, Henning Sprekeler, Jörg Raisch
:
Boosting Fairness and Robustness in Over-the-Air Federated Learning. IEEE Control. Syst. Lett. 8: 682-687 (2024) - Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti:
Robust Federated Learning for Heterogeneous Model and Data. Int. J. Neural Syst. 34(4): 2450019:1-2450019:11 (2024) - Hanchi Ren, Jingjing Deng
, Xianghua Xie
, Xiaoke Ma, Yichuan Wang:
FedBoosting: Federated learning with gradient protected boosting for text recognition. Neurocomputing 569: 127126 (2024) - Ali Akbar Siddique, Nada Alasbali
, Maha Driss
, Wadii Boulila, Mohammed S. Alshehri, Jawad Ahmad
:
Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT). Internet Things 25: 101013 (2024) - Vasileios C. Pezoulas, Fanis G. Kalatzis, Themis P. Exarchos, Andreas Goules, Athanasios G. Tzioufas, Dimitrios I. Fotiadis
:
FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets. Patterns 5(1): 100893 (2024) - Jiao Tian
, Pei-Wei Tsai, Kai Zhang
, Xinyi Cai, Hongwang Xiao, Ke Yu
, Wenyu Zhao
, Jinjun Chen
:
Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost. IEEE Trans. Artif. Intell. 5(2): 647-660 (2024) - Zhihua Tian
, Rui Zhang
, Xiaoyang Hou
, Lingjuan Lyu
, Tianyi Zhang
, Jian Liu
, Kui Ren
:
${\sf FederBoost}$: Private Federated Learning for GBDT. IEEE Trans. Dependable Secur. Comput. 21(3): 1274-1285 (2024) - Wei Xu
, Hui Zhu
, Yandong Zheng
, Fengwei Wang
, Jiaqi Zhao
, Zhe Liu
, Hui Li
:
ELXGB: An Efficient and Privacy-Preserving XGBoost for Vertical Federated Learning. IEEE Trans. Serv. Comput. 17(3): 878-892 (2024) - Mengdi Wang, Anna Bodonhelyi, Efe Bozkir, Enkelejda Kasneci:
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. AAAI 2024: 15546-15554 - Zijun Wang, Hongchen Guo, Keke Gai:
Decision Tree-based Privacy Protection in Federated Learning: A Survey. BigDataSecurity 2024: 119-124 - Cheng-Wei Ching
, Liting Hu
:
Decaffe: DHT Tree-Based Online Federated Fake News Detection. CCEAI 2024: 102-108 - Kotaro Shimamura, Shinya Takamaeda-Yamazaki:
FS-Boost: Communication-Efficient Federated Subtree-Based Gradient Boosting Decision Trees. CCNC 2024: 839-842 - Maryam Ben Driss, Essaid Sabir, Halima Elbiaze, Abdoulaye Baniré Diallo, Mohamed Sadik:
GWO-Boosted Multi-Attribute Client Selection for Over- The-Air Federated Learning. DRCN 2024: 62-69 - Yuhui Zhang, Lutan Zhao, Cheng Che, XiaoFeng Wang, Dan Meng, Rui Hou:
SpecFL: An Efficient Speculative Federated Learning System for Tree-based Model Training. HPCA 2024: 817-831 - Yihang Cheng, Lan Zhang, Junyang Wang, Xiaokai Chu, Dongbo Huang, Lan Xu:
FedMix: Boosting with Data Mixture for Vertical Federated Learning. ICDE 2024: 3379-3392 - Tao Fan, Weijing Chen, Guoqiang Ma, Yan Kang, Lixin Fan, Qiang Yang:
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree. PAKDD (3) 2024: 237-249 - Zhihan Guo
, Yifei Zhang
, Zhuo Zhang
, Zenglin Xu
, Irwin King
:
FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling. WWW (Companion Volume) 2024: 1558-1567 - Timur Sattarov, Marco Schreyer, Damian Borth:
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation. CoRR abs/2401.06263 (2024) - Mengdi Wang, Anna Bodonhelyi, Efe Bozkir, Enkelejda Kasneci:
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. CoRR abs/2401.12012 (2024) - Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice, Markus Kreuzthaler, Martin Urschler:
Federated unsupervised random forest for privacy-preserving patient stratification. CoRR abs/2401.16094 (2024) - Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-kiong Ng:
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework. CoRR abs/2402.12761 (2024) - Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR abs/2402.15070 (2024) - Halil Yigit Öksüz, Fabio Molinari, Henning Sprekeler, Jörg Raisch:
Boosting Fairness and Robustness in Over-the-Air Federated Learning. CoRR abs/2403.04431 (2024) - Alberto Argente-Garrido, Cristina Zuheros, María Victoria Luzón, Francisco Herrera:
An Interpretable Client Decision Tree Aggregation process for Federated Learning. CoRR abs/2404.02510 (2024) - Wenhao Yuan, Xuehe Wang:
QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy. CoRR abs/2404.08261 (2024) - Dayananda Herurkar, Sebastian Palacio, Ahmed Anwar, Jörn Hees, Andreas Dengel:
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data. CoRR abs/2404.14933 (2024) - Niousha Nazemi, Omid Tavallaie, Shuaijun Chen, Albert Y. Zomaya, Ralph Holz:
Boosting Communication Efficiency of Federated Learning's Secure Aggregation. CoRR abs/2405.01144 (2024) - William Lindskog, Christian Prehofer:
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case. CoRR abs/2405.02060 (2024) - William Lindskog, Christian Prehofer, Sarandeep Singh:
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data. CoRR abs/2405.02067 (2024)
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