25 citations · 37 across the 19 of their papers we have counts for
4 papers · 1 filter
TabVFL: Improving Latent Representation in Vertical Federated Learning
Mohamed Rashad, Zilong Zhao, Jeremie Decouchant +1
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model d…
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Bart Cox, Lydia Y. Chen, Jérémie Decouchant
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server. FL relies on clients to update a…
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federico Lucchetti, Jérémie Decouchant, Maria Fernandes +2
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulat…
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Jin Xu, Chi Hong, Jiyue Huang +2
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to…