109 citations · 173 across the 13 of their papers we have counts for
6 papers · 1 filter
Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients
Pratik Agrawal, Philipp Wiesner, Odej Kao
Federated Learning (FL) is a decentralized machine learning approach where local models are trained on distributed clients, allowing privacy-preserving collaboration by sharing mod…
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics
Dominik Scheinert, Philipp Wiesner, Thorsten Wittkopp +3
Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size. As poor choices can have…
Vessim: A Testbed for Carbon-Aware Applications and Systems
Philipp Wiesner, Ilja Behnke, Paul Kilian +2
To reduce the carbon footprint of computing and stabilize electricity grids, there is an increasing focus on approaches that align the power usage of IT infrastructure with the ava…
Towards a Real-Time IoT: Approaches for Incoming Packet Processing in Cyber-Physical Systems
Ilja Behnke, Christoph Blumschein, Robert Danicki +3
Embedded real-time devices for monitoring, controlling, and collaboration purposes in cyber-physical systems are now commonly equipped with IP networking capabilities. However, the…
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning
Thorsten Wittkopp, Dominik Scheinert, Philipp Wiesner +2
Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data suc…