federated learning

PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning AAAI 2023 PerFedMask: Personalized Federated Learning with Optimized Masking Vectors ICLR 2023 Personalized federated domain adaptation for item-to-item recommendation UAI 2023 Personalized Federated Learning under Mixture of Distributions ICML 2023 Personalized Federated Learning with Feature Alignment and Classifier Collaboration ICLR 2023 Personalized Federated Learning with Inferred Collaboration Graphs ICML 2023 Personalized Subgraph Federated Learning ICML 2023 Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning AAAI 2023 PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation AAAI 2023 Practical Takes on Federated Learning with Pretrained Language Models EACL 2023 PRANC: Pseudo RAndom Networks for Compacting Deep Models ICCV 2023 PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023 Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation NeurIPS 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023 Privacy-Preserving Natural Language Processing EACL 2023 Privacy-preserving patient clustering for personalized federated learnings MLHC 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance NeurIPS 2023 Private Federated Learning with Autotuned Compression ICML 2023 Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses ICLR 2023 Private Non-Convex Federated Learning Without a Trusted Server AISTATS 2023 Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data IJCAI 2023 ProtoFL: Unsupervised Federated Learning via Prototypical Distillation ICCV 2023 RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks NeurIPS 2023 Reconstructing Training Data from Model Gradient, Provably AISTATS 2023 Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology ICCV 2023