19 citations · 57 across the 17 of their papers we have counts for
5 papers · 1 filter
MDA: Availability-Aware Federated Learning Client Selection
Amin Eslami Abyane, Steve Drew, Hadi Hemmati
Recently, a new distributed learning scheme called Federated Learning (FL) has been introduced. FL is designed so that server never collects user-owned data meaning it is great at…
Improving the Performance of DNN-based Software Services using Automated Layer Caching
Mohammadamin Abedi, Yanni Iouannou, Pooyan Jamshidi +1
Deep Neural Networks (DNNs) have become an essential component in many application domains including web-based services. A variety of these services require high throughput and (cl…
Test2Vec: An Execution Trace Embedding for Test Case Prioritization
Emad Jabbar, Soheila Zangeneh, Hadi Hemmati +1
Most automated software testing tasks can benefit from the abstract representation of test cases. Traditionally, this is done by encoding test cases based on their code coverage. S…
An Empirical Study on Bug Severity Estimation using Source Code Metrics and Static Analysis
Ehsan Mashhadi, Shaiful Chowdhury, Somayeh Modaberi +2
In the past couple of decades, significant research efforts have been devoted to the prediction of software bugs (i.e., defects). In general, these works leverage a diverse set of…
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
Amin Eslami Abyane, Derui Zhu, Roberto Souza +2
Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are tr…