S&P

2024

• BadVFL: Backdoor Attacks in Vertical Federated Learning.

• LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation.

• Protecting Label Distribution in Cross-Silo Federated Learning.

• FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks.

• SHERPA: Explainable Robust Algorithms for Privacy-preserved Federated Learning in Future Networks to Defend against Data Poisoning Attacks.

2023

• FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information.

• RoFL: Robustness of Secure Federated Learning.

• Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning.

• BayBFed: Bayesian Backdoor Defense for Federated Learning.

• ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems.

• 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning.

• Scalable and Privacy-Preserving Federated Principal Component Analysis.

• ELSA: Secure Aggregation for Federated Learning with Malicious Actors.

2022

• SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs.

• Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning.