"federated learning" Papers
178 papers found • Page 3 of 4
Conference
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
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Sikai Bai, Jie ZHANG, Song Guo et al.
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.
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park 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 Generalized Category Discovery
Nan Pu, Wenjing Li, Xinyuan Ji et al.
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang et al.
Federated Neuro-Symbolic Learning
Pengwei Xing, Songtao Lu, Han Yu
Federated Optimization with Doubly Regularized Drift Correction
Xiaowen Jiang, Anton Rodomanov, Sebastian Stich
Federated Self-Explaining GNNs with Anti-shortcut Augmentations
Linan Yue, Qi Liu, Weibo Gao et al.
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
Xinyuan Ji, Zhaowei Zhu, Wei Xi et al.
FedHARM: Harmonizing Model Architectural Diversity in Federated Learning
Anestis Kastellos, Athanasios Psaltis, Charalampos Z Patrikakis et al.
FedHide: Federated Learning by Hiding in the Neighbors
Hyunsin Park, Sungrack Yun
FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees
Jiahao Liu, Yipeng Zhou, Di Wu et al.
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
Yongzhe Jia, Xuyun Zhang, Amin Beheshti et al.
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Hong Huang, Weiming Zhuang, Chen Chen et al.
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li, Yong Liu, Wei Wang et al.
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Yiyue Chen, Haris Vikalo, Chianing Wang
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Shangchao Su, Bin Li, Xiangyang Xue
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
Yongxin Guo, Xiaoying Tang, Tao Lin
FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error
Yueqi Xie, Minghong Fang, Neil Gong
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
Shusen Jing, Anlan Yu, Shuai Zhang et al.
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
Boyu Fan, Chenrui Wu, Xiang Su et al.
FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation
Fan Qi, Ruijie Pan, Huaiwen Zhang et al.
Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning
Wenke Huang, Mang Ye, zekun shi et al.
Formal Logic Enabled Personalized Federated Learning through Property Inference
Ziyan An, Taylor Johnson, Meiyi Ma
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Aine E Cahill
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma et al.
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi et al.
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Ziqing Fan, Shengchao Hu, Jiangchao Yao et al.