79 citations · 124 across the 5 of their papers we have counts for
5 papers · 1 filter
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning
Aurélien Bellet, Anne-Marie Kermarrec, Erick Lavoie
The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized s…
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…
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…
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
Pierre Dellenbach, Aurélien Bellet, Jan Ramon
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as…
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani +8
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and fra…