41 citations · 102 across the 17 of their papers we have counts for
8 papers · 1 filter
Towards Machine Learning and Inference for Resource-constrained MCUs
Yushan Huang, Hamed Haddadi
Machine learning (ML) is moving towards edge devices. However, ML models with high computational demands and energy consumption pose challenges for ML inference in resource-constra…
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Yacine Belal, Sonia Ben Mokhtar, Hamed Haddadi +2
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an i…
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Siqi Li, Pinyan Liu, Gustavo G. Nascimento +12
Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms…
Analysing Fairness of Privacy-Utility Mobility Models
Yuting Zhan, Hamed Haddadi, Afra Mashhadi
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories. Existing privacy techniques…
Privacy-Aware Adversarial Network in Human Mobility Prediction
Yuting Zhan, Hamed Haddadi, Afra Mashhadi
As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geo…
Towards Battery-Free Machine Learning and Inference in Underwater Environments
Yuchen Zhao, Sayed Saad Afzal, Waleed Akbar +5
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answ…
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh +3
In collaborative learning, clients keep their data private and communicate only the computed gradients of the deep neural network being trained on their local data. Several recent…
Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Lorenzo Minto, Moritz Haller, Hamed Haddadi +1
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the reco…