no code implementations • 25 Jul 2023 • Praveen Joshi, Chandra Thapa, Mohammed Hasanuzzaman, Ted Scully, Haithem Afli
Among various techniques in a DCML framework, federated split learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities.
no code implementations • 7 Apr 2022 • Praveen Joshi, Mohammed Hasanuzzaman, Chandra Thapa, Haithem Afli, Ted Scully
Secondly, this paper presents enabling technologies, such as model parallelism and split learning, which facilitate DL training and deployment at edge servers.
no code implementations • 19 Sep 2021 • Praveen Joshi, Chandra Thapa, Seyit Camtepe, Mohammed Hasanuzzamana, Ted Scully, Haithem Afli
Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning that are gaining attention due to their ability to preserve the privacy of raw data.
no code implementations • 19 Jan 2021 • Javier Escalada, Ted Scully, Francisco Ortin
Moreover, we document the binary patterns used by our classifier to allow their addition in the implementation of existing decompilers.