10 citations · 17 across the 5 of their papers we have counts for
5 papers
On the Conflict of Robustness and Learning in Collaborative Machine Learning
Mathilde Raynal, Carmela Troncoso
Collaborative Machine Learning (CML) allows participants to jointly train a machine learning model while keeping their training data private. In many scenarios where CML is seen as…
Can Decentralized Learning be more robust than Federated Learning?
Mathilde Raynal, Dario Pasquini, Carmela Troncoso
Decentralized Learning (DL) is a peer--to--peer learning approach that allows a group of users to jointly train a machine learning model. To ensure correctness, DL should be robust…
Private Collection Matching Protocols
Kasra EdalatNejad, Mathilde Raynal, Wouter Lueks +1
We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. Existing privac…
On the (In)security of Peer-to-Peer Decentralized Machine Learning
Dario Pasquini, Mathilde Raynal, Carmela Troncoso
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at addressing the main limitations of…
Image Obfuscation for Privacy-Preserving Machine Learning
Mathilde Raynal, Radhakrishna Achanta, Mathias Humbert
Privacy becomes a crucial issue when outsourcing the training of machine learning (ML) models to cloud-based platforms offering machine-learning services. While solutions based on…