396 citations · 601 across the 20 of their papers we have counts for
6 papers · 1 filter
Advancements of federated learning towards privacy preservation: from federated learning to split learning
Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe
In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the lat…
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
Chandra Thapa, Seyit Camtepe
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personal…
Evaluation of Federated Learning in Phishing Email Detection
Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba +5
The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and l…
SplitFed: When Federated Learning Meets Split Learning
Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe +1
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learni…
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim +5
A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively r…
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Yansong Gao, Minki Kim, Sharif Abuadbba +6
This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and…