Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories

no code implementations • 15 Nov 2023 • Shuhei Nitta, Taiji Suzuki, Albert Rodríguez Mulet, Atsushi Yaguchi, Ryusuke Hirai

In this paper, we propose an effective federated learning method named ScalableFL, where the depths and widths of the local models for each client are adjusted according to the clients' input image size and the numbers of output categories.

Federated Learning Image Classification +3