no code implementations • 9 Nov 2022 • Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data.
no code implementations • 28 Jan 2022 • Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky
A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms.
no code implementations • 21 Apr 2021 • Irene Tenison, Sreya Francis, Irina Rish
Federated Averaging (FedAVG) has become the most popular federated learning algorithm due to its simplicity and low communication overhead.
no code implementations • 14 Apr 2021 • Sreya Francis, Irene Tenison, Irina Rish
In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.