432 citations · 437 across the 8 of their papers we have counts for
8 papers
EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning
Di Wu, Rehmat Ullah, Philip Rodgers +3
Efficiently running federated learning (FL) on resource-constrained devices is challenging since they are required to train computationally intensive deep neural networks (DNN) ind…
Simultaneous Transmitting and Reflecting-Reconfigurable Intelligent Surface in 6G: Design Guidelines and Future Perspectives
Waqas Khalid, Zeeshan Kaleem, Rehmat Ullah +3
Reconfigurable intelligent surfaces (RISs) have been considered as a promising technology for the sixth-generation (6G) wireless networks that can control wireless channels in a de…
FedComm: Understanding Communication Protocols for Edge-based Federated Learning
Gary Cleland, Di Wu, Rehmat Ullah +1
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exc…
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Rehmat Ullah, Di Wu, Paul Harvey +3
Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since de…
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Di Wu, Rehmat Ullah, Paul Harvey +3
Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are th…
Information-Centric Networking in Wireless Environments: Security Risks and Challenges
Boubakr Nour, Spyridon Mastorakis, Rehmat Ullah +1
Information-Centric Networking (ICN) has emerged as a paradigm to cope with the lack of built-in security primitives and efficient mechanisms for content distribution of today's In…