79 citations · 128 across the 29 of their papers we have counts for
7 papers · 1 filter
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi +1
Automatic speech recognition (ASR) is a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decodi…
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
Brij Mohan Lal Srivastava, Nathalie Vauquier, Md Sahidullah +3
Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: v…
Private Protocols for U-Statistics in the Local Model and Beyond
James Bell, Aurélien Bellet, Adrià Gascón +1
In this paper, we study the problem of computing -statistics of degree , i.e., quantities that come in the form of averages over pairs of data points, in the local model of d…
metric-learn: Metric Learning Algorithms in Python
William de Vazelhes, CJ Carey, Yuan Tang +2
metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unif…
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
Robin Vogel, Aurélien Bellet, Stephan Clémençon +2
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effor…