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Found 7 papers, 2 papers with code
Solution Simplex Clustering for Heterogeneous Federated Learning
We tackle a major challenge in federated learning (FL) -- achieving good performance under highly heterogeneous client distributions.
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics
However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks.
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing.
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning
Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect.
A Taxonomy of Anomalies in Log Data
In this paper, we present a taxonomy for different kinds of log data anomalies and introduce a method for analyzing such anomalies in labeled datasets.
LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision
With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastructures.
Evaluation of Load Prediction Techniques for Distributed Stream Processing
We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs.
