ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification

1 code implementation • 24 Nov 2023 • Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni

Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the clients and a server.

Federated Learning Image Classification +2