most citedImplicit Model Specialization through DAG-based Decentralized Federated Learning

17 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DC2023★ 16 cited

Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services

Demetris Trihinas, Lauritz Thamsen, Jossekin Beilharz +1

Energy consumption and carbon emissions are expected to be crucial factors for Internet of Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while…

cs.DC2021

Continuously Testing Distributed IoT Systems: An Overview of the State of the Art

Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer +6

The continuous testing of small changes to systems has proven to be useful and is widely adopted in the development of software systems. For this, software is tested in environment…

cs.DC2021★ 17 cited

Implicit Model Specialization through DAG-based Decentralized Federated Learning

Jossekin Beilharz, Bjarne Pfitzner, Robert Schmid +3

Federated learning allows a group of distributed clients to train a common machine learning model on private data. The exchange of model updates is managed either by a central enti…

cs.AI2021

Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring

Kordian Gontarska, Weronika Wrazen, Jossekin Beilharz +3

Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical t…

cs.DC2021

Towards a Staging Environment for the Internet of Things

Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer +5

Internet of Things (IoT) applications promise to make many aspects of our lives more efficient and adaptive through the use of distributed sensing and computing nodes. A central as…