Federated split GANs for collaborative training with heterogeneous devices
Yilei Liang, Pranvera Kortoçi, Pengyuan Zhou, Lik‐Hang Lee, Abbas Mehrabi, Pan Hui, Sasu Tarkoma, Jon Crowcroft · Software Impacts · 2022
Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device heterogeneity, greatly hindering their applicability in practice. In order to address such limitations, we developed a framework based on both FL and SL to share the training load of the discriminative part of a GAN to different client devices. We make our framework available as open-source software 1 .