IJCNN 2016 Special Session

Distributed Learning Algorithms for Neural Networks
IJCNN 2016 Special Session

Download the Call for Papers

Scope and motivation

finalIn the era of big data and pervasive computing, it is common that datasets are distributed over  multiple and geographically distinct sources of information (e.g. distributed databases). In this respect, a major challenge is designing adaptive training algorithms in a distributed fashion, with only partial or no reliance on a centralized authority. Indeed, distributed learning is an important step to handle inference within several research areas, including sensor networks, parallel and commodity computing, distributed optimization, and many others. Additionally, it generalizes previous research on training neural and fuzzy neural models over clusters of processors and, as such, it is crucial in designing training algorithms for efficiently processing large amount of data over networks.

Based on the idea that all the aforementioned research fields share many fundamental questions and mechanisms, this special session is intended to bring forth advances on distributed training for neural networks. We are interested in papers proposing novel algorithms and protocols for distributed training under multiple constraints, analyses of their theoretical aspects, and applications for multiple source data clustering, regression and classification.

  • Paper submission is 15 January 2016.
  • Manuscripts related to the Special Session will be submitted through the IJCNN 2016 paper submission website.
  • For more details, please download the Call for Papers or visit the conference website.


The topics of interest to be covered by this Special Session include, but are not limited to:

  • Distributed algorithms for training neural networks and kernel methods
  • Theoretical aspects of distributed learning (e.g. fundamental communication constraints)
  • Learning on commodity computing architectures and parallel execution frameworks (e.g. MapReduce, Storm)
  • Energy efficient distributed learning
  • Distributed semi-supervised and active learning
  • Novel results on distributed optimization for machine learning
  • Cooperative and competitive multi-agent learning
  • Learning in realistic wireless sensor networks
  • Distributed systems with privacy concerns (e.g. healthcare systems)

Important Dates

  • Paper submission: 15 January 2016.
  • Notification of paper acceptance: 15 March 2016.
  • Camera-ready deadline: 15 April 2016.
  • Conference days: 25-29 July 2016.


For more information, please contact the Special Session organizers:

Prof. Massimo Panella University "La Sapienza", Rome massimo.panella@uniroma1.it
Simone Scardapane University "La Sapienza", Rome simone.scardapane@uniroma1.it

WCCI 2016 is sponsored by the IEEE Computational Intelligence Society.

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  1. Pingback: Distributed Neural Networks@IJCNN 2016 : Special Session on Distributed Learning Algorithms for Neural Networks

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