2024 "data heterogeneity" Papers

14 papers found

A New Theoretical Perspective on Data Heterogeneity in Federated Optimization

Jiayi Wang, Shiqiang Wang, Rong-Rong Chen et al.

ICML 2024poster

Clustered Federated Learning via Gradient-based Partitioning

Heasung Kim, Hyeji Kim, Gustavo De Veciana

ICML 2024poster

FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

Hongyi Peng, Han Yu, Xiaoli Tang et al.

ICML 2024poster

FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants

Shanli Tan, Hao Cheng, Xiaohu Wu et al.

AAAI 2024paperarXiv:2312.11391
6
citations

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

Haokun Chen, Yao Zhang, Denis Krompass et al.

AAAI 2024paperarXiv:2308.12305
86
citations

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

Yongzhe Jia, Xuyun Zhang, Amin Beheshti et al.

AAAI 2024paperarXiv:2402.08578
10
citations

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

Yiyue Chen, Haris Vikalo, Chianing Wang

AAAI 2024paperarXiv:2312.13380
13
citations

Harmonizing Generalization and Personalization in Federated Prompt Learning

Tianyu Cui, Hongxia Li, Jingya Wang et al.

ICML 2024poster

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

Ziqing Fan, Shengchao Hu, Jiangchao Yao et al.

ICML 2024spotlight

On the Role of Server Momentum in Federated Learning

Jianhui Sun, Xidong Wu, Heng Huang et al.

AAAI 2024paperarXiv:2312.12670
22
citations

Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors

Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.

ICML 2024poster

Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective

Yajie Bao, Michael Crawshaw, Mingrui Liu

ICML 2024poster

Ranking-based Client Imitation Selection for Efficient Federated Learning

Chunlin Tian, Zhan Shi, Xinpeng Qin et al.

ICML 2024poster

Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning

Wenke Huang, Zekun Shi, Mang Ye et al.

ICML 2024poster