"federated learning" Papers
178 papers found • Page 4 of 4
Conference
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Luyuan Xie, Manqing Lin, Tianyu Luan et al.
Multi-Dimensional Fair Federated Learning
Cong Su, Guoxian Yu, Jun Wang et al.
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, YANG CAO
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
Nimesh Agrawal, Anuj Sirohi, Sandeep Kumar et al.
On the Role of Server Momentum in Federated Learning
Jianhui Sun, Xidong Wu, Heng Huang et al.
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
Kun Jin, Tongxin Yin, Zhongzhu Chen et al.
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
Yichen Li, Wenchao Xu, Haozhao Wang et al.
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
Bao Li, Zhenyu Liu, Lizhi Shao et al.
PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning
Yuting Ma, Yuanzhi Yao, Xiaohua Xu
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan et al.
Privacy-Preserving Adaptive Re-Identification without Image Transfer
Hamza Rami, Jhony H. Giraldo, Nicolas Winckler et al.
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Roie Reshef, Kfir Levy
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Changyu Gao, Andrew Lowy, Xingyu Zhou et al.
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Yajie Bao, Michael Crawshaw, Mingrui Liu
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
Ranking-based Client Imitation Selection for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin et al.
Recurrent Early Exits for Federated Learning with Heterogeneous Clients
Royson Lee, Javier Fernandez-Marques, Xu Hu et al.
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
Zhen Qin, Feiyi Chen, Chen Zhi et al.
Rethinking the Flat Minima Searching in Federated Learning
Taehwan Lee, Sung Whan Yoon
Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning
Wenke Huang, Zekun Shi, Mang Ye et al.
SILVER: Single-loop variance reduction and application to federated learning
Kazusato Oko, Shunta Akiyama, Denny Wu et al.
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Jiayi Chen, Benteng Ma, Hengfei Cui et al.
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Haibo Yang, Peiwen Qiu, Prashant Khanduri et al.
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun et al.
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
wenlong deng, Christos Thrampoulidis, Xiaoxiao Li
Unveiling Privacy Risks in Stochastic Neural Networks Training: Effective Image Reconstruction from Gradients
Yiming Chen, Xiangyu Yang, Nikos Deligiannis
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Authors: Jinqian Chen, Jihua Zhu, Qinghai Zheng et al.