IEEE International Conference on Computer Communications
17-20 May 2023 // New York area // USA

The Second International IEEE INFOCOM Workshop on Distributed Machine Learning and Fog Networks (FOGML)

FOGML: The Second International IEEE INFOCOM Workshop on Distributed Machine Learning and Fog Networks

Organized in conjunction with

IEEE International Conference on Computer Communications, May 20, 2023

 

CALL FOR PAPERS        COMMITTEE        PROGRAM

 

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. This includes research efforts in developing machine learning methodologies both "for" and "over" networks along the cloud-to-things continuum.

 

Paper Submission Link

https://edas.info/N30350

 

Important Dates

Submission Deadline: December 20, 2022

Notification of Acceptance: February 6, 2023

Camera Ready Deadline: March 6, 2023

Workshop: May 20, 2023

Patrons