Knowledge Distillation for Federated Learning: a Practical Guide

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.

Federated Learning Knowledge Distillation

Gradient Masked Averaging for Federated Learning

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.

Federated Learning Out-of-Distribution Generalization

Gradient Masked Federated Optimization

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.

Federated Learning

Towards Causal Federated Learning For Enhanced Robustness and Privacy

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.

Federated Learning Privacy Preserving