CCS

2024

• Toward Byzantine-Robust Decentralized Federated Learning.

• Cross-silo Federated Learning with Record-level Personalized Differential Privacy.

• Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation.

• Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses.

• Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning.

• Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy.

• Samplable Anonymous Aggregation for Private Federated Data Analysis.

2023

• martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture.

• Turning Privacy-preserving Mechanisms against Federated Learning.

• Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks.

• MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers.

2022

• Federated Boosted Decision Trees with Differential Privacy.

• CERBERUS: Exploring Federated Prediction of Security Events.

• Eluding Secure Aggregation in Federated Learning via Model Inconsistency.

• EIFFeL: Ensuring Integrity for Federated Learning.