1 code implementation • 16 Feb 2023 • Ege Erdogan, Unat Teksen, Mehmet Salih Celiktenyildiz, Alptekin Kupcu, A. Ercument Cicek
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server.
1 code implementation • 20 Aug 2021 • Ege Erdogan, Alptekin Kupcu, A. Ercument Cicek
Distributed deep learning frameworks such as split learning provide great benefits with regards to the computational cost of training deep neural networks and the privacy-aware utilization of the collective data of a group of data-holders.
1 code implementation • 20 Aug 2021 • Ege Erdogan, Alptekin Kupcu, A. Ercument Cicek
(1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and obtain a functionally similar model to the client model, without being detected.
1 code implementation • 12 Feb 2019 • Can Firtina, Jeremie S. Kim, Mohammed Alser, Damla Senol Cali, A. Ercument Cicek, Can Alkan, Onur Mutlu
Our experiments with real read sets demonstrate that Apollo is the only algorithm that 1) uses reads from any sequencing technology within a single run and 2) scales well to polish large assemblies without splitting the assembly into multiple parts.