Poster "data heterogeneity" Papers
37 papers found
Conference
Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning
Mingyuan Fan, Zhanyi Hu, Fuyi Wang et al.
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.
DEPT: Decoupled Embeddings for Pre-training Language Models
Alex Iacob, Lorenzo Sani, Meghdad Kurmanji 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.
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
Bicheng Ying, Zhe Li, Haibo Yang
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
Changlong Shi, He Zhao, Bingjie Zhang et al.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang et al.
FedEL: Federated Elastic Learning for Heterogeneous Devices
Letian Zhang, Bo Chen, Jieming Bian et al.
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu et al.
Federated Residual Low-Rank Adaption of Large Language Models
Yunlu Yan, Chun-Mei Feng, Wangmeng Zuo 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.
Flick: Empowering Federated Learning with Commonsense Knowledge
Ran Zhu, Mingkun Yang, Shiqiang Wang et al.
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
Yanbiao Ma, Wei Dai, Wenke Huang et al.
Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch
Yijie Liu, Xinyi Shang, Yiqun Zhang 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.
Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
Sheng Huang, Lele Fu, Fanghua Ye 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.
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
Shanshan Yan, Zexi Li, Chao Wu 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.
Towards Efficient Replay in Federated Incremental Learning
Yichen Li, Qunwei Li, Haozhao Wang et al.
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun et al.
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
wenlong deng, Christos Thrampoulidis, Xiaoxiao Li