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Found 7 papers, 0 papers with code
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning
Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself.
Matching DNN Compression and Cooperative Training with Resources and Data Availability
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance.
Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN
Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces.
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum
In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available.
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost
Traditionally, distributed machine learning takes the guise of (i) different nodes training the same model (as in federated learning), or (ii) one model being split among multiple nodes (as in distributed stochastic gradient descent).
Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G
5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization.
Networking and Internet Architecture
Network Support for High-performance Distributed Machine Learning
We then formulate the problem of selecting (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of iterations to perform, in order to minimize the learning cost while meeting the target prediction error and execution time.
