arXiv AI
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 16
Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning
arXiv:2606. 17035v1 Announce Type: new Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks.
arXiv Machine Learning
1d ago
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
arXiv:2601.06641v4 Announce Type: replace Abstract: Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard...
arXiv AI
Jul 20
Poison to Detect: Detection of Targeted Overfitting in Federated Learning
arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).
arXiv Machine Learning
Jul 21
Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.
arXiv Machine Learning
Aug 24
BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning
arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...
arXiv Machine Learning
Jun 17
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.