We are currently soliciting bid proposals to host the future International Conference on Data Science and Advanced Analytics (DSAA’2024 and DSAA’2025). DSAA is the only data science event that is jointly sponsored by IEEE, ACM, and ASA. More information on DSAA and the call for bids is available here:


If you're looking to further your research or access research datasets in the area of computational intelligence, take advantage of your free individual subscription to IEEE DataPort. All CIS members automatically have a free individual subscription to IEEE DataPort, the go-to resource for accessing, storing, and managing research data. Just login to IEEE DataPort using your society member credentials to activate your free individual subscription.

IEEE DataPort has over 4 million global users and over 4,400 datasets and with your free individual subscription you will get free access to all of the 4,400+ datasets in the cloud or by direct download. IEEE DataPort datasets have a CC-BY license so datasets can be copied, analyzed, or used for any other purpose with proper attribution.

There are already more than 200 computational intelligence datasets available to view and download so login to IEEE DataPort today!


IEEE DataPort also allows CIS members to upload and store research data indefinitely, obtain a DOI, link to a paper, generate citations, and more – all at no charge. If you have valuable research data that could benefit the global technical community, upload your own research data . Storing your research data on IEEE DataPort helps gain citations and exposure for your research. We’d love to add your research to the growing collection of datasets on IEEE DataPort.

Just login to IEEE DataPort using your society member credentials to activate your free individual subscription, a $480 annual value. We look forward to adding more members of the Computational Intelligence Society to the IEEE DataPort community.

IEEESA Call for participation

IEEE Standards Association (IEEE SA) invites you to participate in the Working Group for IEEE P3187™, Guide for Framework for Trustworthy Federated Machine Learning.




This guide provides a reference framework for trustworthy Federated Machine Learning. The document provides guidance with respect to provable security for data and models, optimized model utility, controllable communication and computational complexity, explainable decision making and supervised processes. It describes three main aspects:

  1. Principles for trustworthy Federated Machine Learning
  2. Requirements for different roles in trustworthy Federated Machine Learning
  3. Techniques to realize trustworthy Federated Machine Learning

The purpose of this guide is to provide credible, practical and controllable solution guidance for Federated Machine Learning and other privacy computing applications.

For additional information, contact the IEEE P3187™ Working Group Chair, Zuping Wu , at or the IEEE SA Program Manager, Christy Bahn , at .