Search Results for author: Ted Scully

Found 4 papers, 0 papers with code

Federated Split Learning with Only Positive Labels for resource-constrained IoT environment

no code implementations25 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.

Enabling All In-Edge Deep Learning: A Literature Review

no code implementations7 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.

Edge-computing Model Compression +3

Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance

no code implementations19 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.

Federated Learning Image Classification +1

Improving type information inferred by decompilers with supervised machine learning

no code implementations19 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.

BIG-bench Machine Learning Vocal Bursts Type Prediction

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