2025 "data heterogeneity" Papers

Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning

Dipam Goswami, Simone Magistri, Kai Wang et al.

Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees

Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis et al.

DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning

Yueyang Yuan, Wenke Huang, Guancheng Wan 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.

SparsyFed: Sparse Adaptive Federated Learning

Adriano Guastella, Lorenzo Sani, Alex Iacob et al.

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Xinghao Wu, Xuefeng Liu, Jianwei Niu et al.

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