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Found 10 papers, 3 papers with code
Empowering Data Mesh with Federated Learning
To the best of our knowledge, this is the first open-source applied work that represents a critical advancement toward the integration of federated learning methods into the Data Mesh paradigm, underscoring the promising prospects for privacy-preserving and decentralized data analysis strategies within Data Mesh architecture.
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities
We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed.
Toward efficient resource utilization at edge nodes in federated learning
In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round.
FedBot: Enhancing Privacy in Chatbots with Federated Learning
The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training.
Accelerating Fair Federated Learning: Adaptive Federated Adam
This is known as the fairness problem in federated learning.
FedQAS: Privacy-aware machine reading comprehension with federated learning
In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
Scalable federated machine learning with FEDn
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
Understanding the Quality of Container Security Vulnerability Detection Tools
Therefore, in this study, we investigate the quality of existing container scanning tools by proposing two metrics that reflects coverage and accuracy.
Vulnerability Detection
Cryptography and Security
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets
This problem is more visible in the context of medium and small scale data center operators (the long tail of e-infrastructure providers).
Distributed, Parallel, and Cluster Computing
Apache Spark Streaming and HarmonicIO: A Performance and Architecture Comparison
Studies have demonstrated that Apache Spark, Flink and related frameworks can perform stream processing at very high frequencies, whilst tending to focus on small messages with a computationally light `map' stage for each message; a common enterprise use case.
Distributed, Parallel, and Cluster Computing
