ICML 2024 "data heterogeneity" Papers
9 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
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
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