FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning

4 February 2021

Papers citing "FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning"

22 / 22 papers shown

Title
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions Flora Amato Lingyu Qiu Mohammad Tanveer S. Cuomo F. Giampaolo F. Piccialli FedML 58 0 0 05 May 2025
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning Jun Bai Yiliao Song Di Wu Atul Sajjanhar Yong Xiang Wei Zhou Xiaohui Tao Yan Li Y. Li FedML 53 0 0 28 Oct 2024
Federated Learning with Label-Masking Distillation Jianghu Lu Shikun Li Kexin Bao Pengju Wang Zhenxing Qian Shiming Ge FedML 39 10 0 20 Sep 2024
Federated Model Heterogeneous Matryoshka Representation Learning Liping Yi Han Yu Chao Ren Gang Wang Xiaoguang Liu Xiaoxiao Li FedML 43 8 0 01 Jun 2024
Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data Mahdi Morafah M. Reisser Bill Lin Christos Louizos FedML 34 5 0 13 May 2024
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning Changlin Song Divya Saxena Jiannong Cao Yuqing Zhao FedML 34 3 0 14 Apr 2024
Federated Learning over Connected Modes Dennis Grinwald Philipp Wiesner Shinichi Nakajima FedML 45 0 0 05 Mar 2024
Practical Insights into Knowledge Distillation for Pre-Trained Models Norah Alballa Marco Canini 42 2 0 22 Feb 2024
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer S. Pokutta AAML 49 1 0 19 Feb 2024
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning Ye Lin Tun Chu Myaet Thwal Le Quang Huy Minh N. H. Nguyen Choong Seon Hong FedML 38 2 0 22 Jan 2024
Teacher-Student Architecture for Knowledge Distillation: A Survey Chengming Hu Xuan Li Danyang Liu Haolun Wu Xi Chen Ju Wang Xue Liu 21 16 0 08 Aug 2023
Exploiting Features and Logits in Heterogeneous Federated Learning Yun-Hin Chan Edith C. H. Ngai FedML 24 2 0 27 Oct 2022
Towards Efficient Communications in Federated Learning: A Contemporary Survey Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 51 59 0 02 Aug 2022
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning Lin Zhang Li Shen Liang Ding Dacheng Tao Ling-Yu Duan FedML 28 252 0 17 Mar 2022
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data Y. Cheung Juyong Jiang F. Yu Jian Lou FedML 24 12 0 03 Mar 2022
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models Lan Zhang Dapeng Oliver Wu Xiaoyong Yuan FedML 22 47 0 08 Sep 2021
Reward-Based 1-bit Compressed Federated Distillation on Blockchain Leon Witt Usama Zafar KuoYeh Shen Felix Sattler Dan Li Wojciech Samek FedML 24 4 0 27 Jun 2021
Data-Free Knowledge Distillation for Heterogeneous Federated Learning Zhuangdi Zhu Junyuan Hong Jiayu Zhou FedML 22 627 0 20 May 2021
Federated Unsupervised Representation Learning Fengda Zhang Kun Kuang Zhaoyang You T. Shen Jun Xiao Yin Zhang Chao-Xiang Wu Yueting Zhuang Xiaolin Li FedML 22 134 0 18 Oct 2020
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models Zhengming Zhang Yaoqing Yang Z. Yao Yujun Yan Joseph E. Gonzalez Michael W. Mahoney FedML 31 36 0 26 Aug 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 179 1,032 0 29 Nov 2018
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 278 404 0 09 Apr 2018