"federated learning" Papers
87 papers found • Page 1 of 2
Connecting Federated ADMM to Bayes
Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez
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.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang 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.
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.
FedRACE: A Hierarchical and Statistical Framework for Robust Federated Learning
Gang Yan, Sikai Yang, Wan Du
FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
Pukang Ye, Luo Junwei, Jiachen Shen et al.
Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
Ye Li, Yanchao Zhao, chengcheng zhu et al.
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model
Ziyuan Yang, Yingyu Chen, Zhiwen Wang et al.
Query-based Knowledge Transfer for Heterogeneous Learning Environments
Norah Alballa, Wenxuan Zhang, Ziquan Liu et al.
Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
Arian Raje, Baris Askin, Divyansh Jhunjhunwala et al.
Sketched Gaussian Mechanism for Private Federated Learning
Qiaobo Li, Zhijie Chen, Arindam Banerjee
SPFL: Sequential updates with Parallel aggregation for Enhanced Federated Learning under Category and Domain Shifts
Haoyuan Liang, Shilei Cao, Li et al.
Streaming Federated Learning with Markovian Data
Khiem HUYNH, Malcolm Egan, Giovanni Neglia et al.
Accelerating Federated Learning with Quick Distributed Mean Estimation
Ran Ben Basat, Shay Vargaftik, Amit Portnoy et al.
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo et al.
Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
Do-Yeon Kim, Dong-Jun Han, Jun Seo et al.
Adaptive Group Personalization for Federated Mutual Transfer Learning
Haoqing Xu, Dian Shen, Meng Wang et al.
A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
Hongchang Gao
AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning
Dong Chen, Hongyuan Qu, Guangwu Xu
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization
Xinwen Zhang, Ali Payani, Myungjin Lee et al.
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen et al.
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi et al.
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Mengmeng Ma, Tang Li, Xi Peng
Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
Jiaqi Wang, Chenxu Zhao, Lingjuan Lyu et al.
Byzantine Resilient and Fast Federated Few-Shot Learning
Ankit Pratap Singh, Namrata Vaswani
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui et al.
Certifiably Byzantine-Robust Federated Conformal Prediction
Mintong Kang, Zhen Lin, Jimeng Sun et al.
Clustered Federated Learning via Gradient-based Partitioning
Heasung Kim, Hyeji Kim, Gustavo De Veciana
COALA: A Practical and Vision-Centric Federated Learning Platform
Weiming Zhuang, Jian Xu, Chen Chen et al.
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
Tianyu Guo, Sai Praneeth Karimireddy, Michael Jordan
Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models
Zixin Zhang, Fan Qi, Changsheng Xu
Exploiting Label Skews in Federated Learning with Model Concatenation
Yiqun Diao, Qinbin Li, Bingsheng He
FADAS: Towards Federated Adaptive Asynchronous Optimization
Yujia Wang, Shiqiang Wang, Songtao Lu et al.
Fair Federated Learning via the Proportional Veto Core
Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan et al.
FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update
Ji Liu, Juncheng Jia, Tianshi Che et al.
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang et al.
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
Jingwei Sun, Ziyue Xu, Hongxu Yin et al.
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Hongyi Peng, Han Yu, Xiaoli Tang et al.
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan, Hao Cheng, Xiaohu Wu et al.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Haokun Chen, Yao Zhang, Denis Krompass et al.
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
Authors: Jichang Li, Guanbin Li, Hui Cheng et al.
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
Shangchao Su, Mingzhao Yang, Bin Li et al.
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang et al.
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Zhen Qin, Daoyuan Chen, Bingchen Qian et al.
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang et al.