Poster "data heterogeneity" Papers
20 papers found
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis et al.
DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity
Zhen Qin, Zhuqing Liu, Songtao Lu et al.
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan et al.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang et al.
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu et al.
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Chenxin Li et al.
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
Hong Huang, Jinhai Yang, Yuan Chen et al.
FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization
Seung-Wook Kim, Seongyeol Kim, Jiah Kim et al.
Problem-Parameter-Free Federated Learning
Wenjing Yan, Kai Zhang, Xiaolu Wang et al.
Query-based Knowledge Transfer for Heterogeneous Learning Environments
Norah Alballa, Wenxuan Zhang, Ziquan Liu et al.
Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
Kumar Kshitij Patel, Ali Zindari, Sebastian Stich et al.
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Xuefeng Liu, Jianwei Niu et al.
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen et al.
Clustered Federated Learning via Gradient-based Partitioning
Heasung Kim, Hyeji Kim, Gustavo De Veciana
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Hongyi Peng, Han Yu, Xiaoli Tang et al.
Harmonizing Generalization and Personalization in Federated Prompt Learning
Tianyu Cui, Hongxia Li, Jingya Wang et al.
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Yajie Bao, Michael Crawshaw, Mingrui Liu
Ranking-based Client Imitation Selection for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin et al.
Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning
Wenke Huang, Zekun Shi, Mang Ye et al.