Federated learning usually employs a client-server architecture where an orchestrator
iteratively aggregates model updates from remote clients and pushes them back a refined …

Large scientific data transfers often occur at high rates causing increased burstiness in
Internet traffic. To limit the adverse effects of these high-rate large-sized flows, which are …

The evolution of mechanisms for providing Quality-of-Service (QoS) over the contemporary
network infrastructures has introduced the need for regulation and management of the …

With the rapid development of data‐driven and bandwidth‐intensive applications in the
Software Defined Networking (SDN) northbound interface, big data stream is dynamically …

Combining the existing approaches for optical intelligence and the speed and capacity of
light is undoubtedly the only viable strategy for building future-proof, highspeed networks …

Federated Learning (FL) stands as a framework facilitating geographically distributed clients
to collaboratively learn machine learning models without divulging their local data. This …

This paper describes the design and the implementation of QoS services in a high speed
backbone network as well as a management tool for the service. The services were …

Im Zentrum dieser Arbeit stehen die Wettbewerbspotenziale in Netzen. Zu-nächst wird der
Netzwettbewerb im Spannungsfeld zwischen Größenvorteilen, Vielfalt und Netzevolutorik …

A common denominator of network neutrality definitions is a plea against traffic shaping
within the Internet and the resultant challenge to the traditional best effort transmission. By …

Die bekannteste Internet-Architektur, die für die Bereitstellung von Qualitäts-klassen des
Datentransports in Zugangs-und Kernnetzen entwickelt wurde, ist die Differenzierte Dienste …