"data heterogeneity" Papers

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.

FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning

Zhiqin Yang, Yonggang Zhang, Chenxin Li et al.

Query-based Knowledge Transfer for Heterogeneous Learning Environments

Norah Alballa, Wenxuan Zhang, Ziquan Liu et al.

Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Training

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.

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.

FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing

Yongzhe Jia, Xuyun Zhang, Amin Beheshti et al.

Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity

Yiyue Chen, Haris Vikalo, Chianing Wang

Harmonizing Generalization and Personalization in Federated Prompt Learning

Tianyu Cui, Hongxia Li, Jingya Wang et al.

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

Ziqing Fan, Shengchao Hu, Jiangchao Yao 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.

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.