Federated Learning

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 PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance NeurIPS 2023 Private Federated Learning with Autotuned Compression ICML 2023 Private Non-Convex Federated Learning Without a Trusted Server AISTATS 2023 Reconstructing Training Data from Model Gradient, Provably AISTATS 2023 Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology ICCV 2023 Re-Thinking Federated Active Learning Based on Inter-Class Diversity CVPR 2023 Revisiting Weighted Aggregation in Federated Learning with Neural Networks ICML 2023 Robust and Actively Secure Serverless Collaborative Learning NeurIPS 2023 Secure Federated Correlation Test and Entropy Estimation ICML 2023 Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning AAAI 2023 SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning NeurIPS 2023 Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability ICML 2023 Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning ICML 2023 TabLeak: Tabular Data Leakage in Federated Learning ICML 2023 Tackling Data Heterogeneity in Federated Learning with Class Prototypes AAAI 2023 The Aggregation–Heterogeneity Trade-off in Federated Learning COLT 2023 The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond ICML 2023 The communication cost of security and privacy in federated frequency estimation AISTATS 2023 Towards Attack-tolerant Federated Learning via Critical Parameter Analysis ICCV 2023 Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning NeurIPS 2023 Towards Instance-adaptive Inference for Federated Learning ICCV 2023