ESORICS

2024

• Fake or Compromised? Making Sense of Malicious Clients in Federated Learning.

• Exploiting Internal Randomness for Privacy in Vertical Federated Learning.

• VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification.

• Exploiting Layerwise Feature Representation Similarity For Backdoor Defence in Federated Learning.

2023

• FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models.

• Exploiting Internal Randomness for Privacy in Vertical Federated Learning.

2022

• FLMJR: Improving Robustness of Federated Learning via Model Stability.

• Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning.

• Local Differential Privacy for Federated Learning in Industrial Settings.