151 citations · 373 across the 31 of their papers we have counts for
9 papers · 1 filter
Fully Decentralized Certified Unlearning
Hithem Lamri, Michail Maniatakos
Machine unlearning (MU) seeks to remove the influence of specified data from a trained model in response to privacy requests or data poisoning. While certified unlearning has been…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…
DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems
Hithem Lamri, Manaar Alam, Haiyan Jiang +1
Federated Unlearning (FU) enables clients to remove the influence of specific data from a collaboratively trained shared global model, addressing regulatory requirements such as GD…
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning
Manaar Alam, Hithem Lamri, Michail Maniatakos
Federated Learning (FL) enables collaborative deep learning training across multiple participants without exposing sensitive personal data. However, the distributed nature of FL an…
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos +1
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due…
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems
Yue Wang, Wending Li, Michail Maniatakos +1
Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous Vehicles (AV), but also makes them susceptible to backdoor attacks that can result in traffic congestion or…
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
Yue Wang, Wenqing Li, Esha Sarkar +3
Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs t…
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code
Constantine Doumanidis, Prashant Hari Narayan Rajput, Michail Maniatakos
Industrial Control Systems (ICS) have played a catalytic role in enabling the 4th Industrial Revolution. ICS devices like Programmable Logic Controllers (PLCs), automate, monitor,…
TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis
Esha Sarkar, Michail Maniatakos
Machine Learning (ML) has achieved unprecedented performance in several applications including image, speech, text, and data analysis. Use of ML to understand underlying patterns i…