The First International Workshop on Distributed Machine Learning and Fog Networks
in conjunction with IEEE INFOCOM 2021
10 May 2021 // Virtual Workshop
Fog networking is emerging as an end-to-end architecture that aims to distribute computing, storage, control, and networking functions along the cloud-to-things continuum of nodes that exists between datacenters and end users. Fueled by the volumes of data generated by network devices, machine learning has attracted significant attention in fog computing systems, both for providing intelligent applications to end users and for optimizing the operation of wireless and wireline networks. Existing methodologies for distributing machine learning across a set of devices have typically been envisioned for scenarios where device communication and computation properties are homogeneous, and/or where devices are directly connected to an aggregation server. These assumptions often do not hold in contemporary fog network systems, however. This motivates a new paradigm of fog learning to distribute model training over networks in a network-aware manner, i.e., considering the structure of the topology among devices, the heterogeneity of node communication and computation capabilities, and the proximity of resource-limited to resource-abundant nodes to optimize training. It also motivates the development of novel machine learning techniques to optimize the operation of fog network systems, which must consider the short timescale variability in network state due to device mobility.
The International IEEE Workshop on Distributed Machine Learning and Fog Networks (FOGML) aims to bring together researchers, developers, and practitioners from academia and industry to innovate at the intersection of distributed machine learning and fog computing.
Submission Deadline: December 15, 2020
Notification of Acceptance: January 15, 2021
Camera Ready: February 15, 2021
Workshop Date: May 10, 2021