← Learning Types
Deep Learning › Learning Types ›

Federated Learning

164 directly classified papers

Papers per year

Papers

Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method NIPS 2024
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion NIPS 2024
z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning AAAI 2024
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA EMNLP 2024
Enhancing Byzantine-Resistant Aggregations with Client Embedding EMNLP 2024
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems EMNLP 2024
User Inference Attacks on Large Language Models EMNLP 2024
Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms AAAI 2024
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning NIPS 2023
When Do Curricula Work in Federated Learning? ICCV 2023
Revisiting Weighted Aggregation in Federated Learning with Neural Networks ICML 2023
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning IJCAI 2023
Tackling Data Heterogeneity in Federated Learning with Class Prototypes AAAI 2023
Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning AAAI 2023
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning AAAI 2023
Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning AAAI 2023
Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning AAAI 2023
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing AAAI 2023
FedABC: Targeting Fair Competition in Personalized Federated Learning AAAI 2023
Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework AAAI 2023
Federated Generative Model on Multi-Source Heterogeneous Data in IoT AAAI 2023
On the Vulnerability of Backdoor Defenses for Federated Learning AAAI 2023
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models ACL 2023
On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data CVPR 2023
Elastic Aggregation for Federated Optimization CVPR 2023