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Federated Learning

551 directly classified papers

Papers per year

Papers

Federated Learning With Data-Agnostic Distribution Fusion CVPR 2023
Decentralized Learning With Multi-Headed Distillation CVPR 2023
Client-Customized Adaptation for Parameter-Efficient Federated Learning ACL 2023
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter ACL 2023
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models ACL 2023
Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification EMNLP 2023
Improving the Model Consistency of Decentralized Federated Learning ICML 2023
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? JMLR 2023
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points JMLR 2023
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning JMLR 2023
Federated Online and Bandit Convex Optimization ICML 2023
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning CVPR 2023
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation CVPR 2023
Fair Federated Medical Image Segmentation via Client Contribution Estimation CVPR 2023
Efficient On-Device Training via Gradient Filtering CVPR 2023
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates JMLR 2023
FedLab: A Flexible Federated Learning Framework JMLR 2023
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction NIPS 2023
Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning NIPS 2023
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NIPS 2023
H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets NIPS 2023
Nothing but Regrets — Privacy-Preserving Federated Causal Discovery AISTATS 2023
Revisiting Weighted Aggregation in Federated Learning with Neural Networks ICML 2023
Federated Adversarial Learning: A Framework with Convergence Analysis ICML 2023
Bayesian Federated Neural Matching That Completes Full Information AAAI 2023