Resource-Constrained Learning in Wireless Networks

This workshop seeks to bring ML and wireless networking experts together to identify interdisciplinary approaches to evolve ML algorithms for and over communication networks that operate under constrained resources, including time, labeling, and computational capacity constraints. The workshop will provide a unique opportunity to expose the MLSys community to the challenges and opportunities of integrating ML methods into resource-constrained communication networks. It will also highlight emerging trends in ML with limited resources and their implications for the design and operation of next-generation communication networks.

We are seeking original submissions in topics including, but not limited to:

- Learning in wireless networks with limited training data
- Multi-agent federated/distributed learning with low computational and communication resources
- Communicated data compression for network-wide task completion
- Online learning with wireless latency constraints
- Learning in wireless networks with privacy constraints
- Few-shot learning and adaptation in wireless environments
- Datasets and benchmarks for resource-constrained learning in wireless networks