Poster "federated learning" Papers
132 papers found • Page 1 of 3
Conference
A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
Siyuan Wu, Yongzhe Jia, Haolong Xiang et al.
Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh et al.
BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
Haozhao Wang, Shengyu Wang, Jiaming Li et al.
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng et al.
Connecting Federated ADMM to Bayes
Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez
Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
Dipam Goswami, Simone Magistri, Kai Wang et al.
Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu et al.
Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
Ming Wen, Jiaqi Zhu, Yuedong Xu et al.
Diffusion Federated Dataset
SEOKJU HAHN, Junghye Lee
DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning
Yueyang Yuan, Wenke Huang, Guancheng Wan et al.
Efficient Adaptive Federated Optimization
Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer et al.
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan et al.
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu et al.
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
Bicheng Ying, Zhe Li, Haibo Yang
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
Changlong Shi, He Zhao, Bingjie Zhang et al.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang et al.
FedEL: Federated Elastic Learning for Heterogeneous Devices
Letian Zhang, Bo Chen, Jieming Bian et al.
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu et al.
Federated Domain Generalization with Data-free On-server Matching Gradient
Binh Nguyen, Minh-Duong Nguyen, Jinsun Park et al.
Federated Few-Shot Class-Incremental Learning
Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu et al.
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang et al.
Federated Learning with Domain Shift Eraser
Zheng Wang, Zihui Wang, Zheng Wang et al.
Federated Residual Low-Rank Adaption of Large Language Models
Yunlu Yan, Chun-Mei Feng, Wangmeng Zuo et al.
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
Li Zhang, Zhongxuan Han, XiaoHua Feng et al.
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Chenxin Li et al.
FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Geeho Kim, Jinu Lee, Bohyung Han
FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
Changlong Shi, Jinmeng Li, He Zhao et al.
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
Mainak Singha, Subhankar Roy, Sarthak Mehrotra et al.
FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Zhihang Deng et al.
FedRACE: A Hierarchical and Statistical Framework for Robust Federated Learning
Gang Yan, Sikai Yang, Wan Du
FedRAM: Federated Reweighting and Aggregation for Multi-Task Learning
Fan Wu, Xinyu Yan, Jiabei Liu et al.
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
Hong Huang, Jinhai Yang, Yuan Chen et al.
FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
Pukang Ye, Luo Junwei, Jiachen Shen et al.
FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation
Cui Miao, Tao Chang, meihan wu et al.
FedWMSAM: Fast and Flat Federated Learning via Weighted Momentum and Sharpness-Aware Minimization
Tianle Li, Yongzhi Huang, Linshan Jiang et al.
FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization
Seung-Wook Kim, Seongyeol Kim, Jiah Kim et al.
Flick: Empowering Federated Learning with Commonsense Knowledge
Ran Zhu, Mingkun Yang, Shiqiang Wang et al.
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Yan Gao, Massimo R. Scamarcia, Javier Fernandez-Marques et al.
Gains: Fine-grained Federated Domain Adaptation in Open Set
Zhengyi Zhong, Wenzheng Jiang, Weidong Bao et al.
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
Yanbiao Ma, Wei Dai, Wenke Huang et al.
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection
Lei Shen, Zhenheng Tang, Lijun Wu et al.
Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
Ye Li, Yanchao Zhao, chengcheng zhu et al.
Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
Jisoo Kim, Sungmin Kang, Sunwoo Lee
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
Yehya Farhat, Hamza ElMokhtar Shili, Fangshuo Liao et al.
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
Jieming Bian, Lei Wang, Letian Zhang et al.
Many-Objective Multi-Solution Transport
Ziyue Li, Tian Li, Virginia Smith et al.
MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Ning Yuxuan, Zhicong Huang et al.
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou
NoT: Federated Unlearning via Weight Negation
Yasser Khalil, Leo Maxime Brunswic, Soufiane Lamghari et al.
Optimization with Access to Auxiliary Information
EL MAHDI CHAYTI, Sai Karimireddy