Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies

Autor: Fernandez, Joaquin Delgado, Brennecke, Martin, Barbereau, Tom, Rieger, Alexander, Fridgen, Gilbert

Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to share thei

Externí odkaz: http://arxiv.org/abs/2308.02219

Beyond a Fistful of Tumblers: Toward a Taxonomy of Ethereum-based Mixers.

Autor: Barbereau, Tom, Ermolaev, Egor, Brennecke, Martin, Hartwich, Eduard, Sedlmei, Johannes

Publikováno v: Proceedings of the International Conference on Information Systems (ICIS); 2023, p1-17, 17p

Meeting Grid Code Requirements of Decentralized Power Generators and Plants by Using Voltage Regulating Distribution Transformers as Cost-Efficient Component.

Autor: Langstadtler, Julian, Döll, Johannes, Brennecke, Martin, Smolka, Thomas, Sojer, Manuel, Schlegel, Thomas, Funk, Tobias

Publikováno v: Proceedings of the 13th International Workshop on Large - Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Plants; 2014, p613-617, 5p