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<p><b>IEEE BIGDATA 2026 - 3rd Special Session on Federated
Learning on Big Data<br>
</b></p>
<p><b>Organizers</b><br>
</p>
<p>Prof. Francesco Piccialli, University of Naples Federico II,
Italy</p>
<p>Dr. Daniela Annunziata, University of Naples Federico II, Italy</p>
<p>Dr. Fabio Giampaolo, University of Naples Federico II, Italy</p>
<p>Prof. David Camacho, Universidad Politecnica de Matrid, Spain<br>
<br>
</p>
<p><b>Official Link: </b><a class="moz-txt-link-freetext" href="https://bigdataieee.org/BigData2026/calls/special-federated-learning/">https://bigdataieee.org/BigData2026/calls/special-federated-learning/</a></p>
<p><b>Selected papers will be invited to submit an extended
contribution to some top-ranking journals</b></p>
<p><b>Aim and Scope</b><br>
<br>
The "Special Session on Federated Learning on Big Data" aims to
bring together researchers, industry practitioners, and
policymakers to explore cutting-edge advancements and address
pressing challenges in the application of federated learning to
Big Data. Federated learning is revolutionizing the way
organizations handle machine learning across distributed data
sources, enabling collaborative model training without
compromising data privacy. With the proliferation of data from
various sources such as healthcare, finance, IoT, and
multimedia, this session provides an invaluable opportunity to
delve into the practical and theoretical aspects of federated
learning, focusing on its integration with the 5Vs of Big Data:
Volume, Velocity, Variety, Value, and Veracity.<br>
<br>
The session will highlight recent innovations in federated
learning algorithms and frameworks designed to handle the unique
challenges posed by Big Data, such as heterogeneous data
distributions and resource constraints. Furthermore, it will
explore the interplay between federated learning and
privacy-preserving mechanisms, ensuring secure data exchange
across institutions and organizations. Special emphasis will be
placed on real-world applications in healthcare, IoT, and
finance, where federated learning allows organizations to
harness the potential of decentralized data while respecting
privacy regulations.<br>
<br>
We aim to foster cross-disciplinary collaboration and
knowledge-sharing that leads to new methods, architectures, and
systems that push the boundaries of federated learning research.
This session will also shed light on the emerging policy and
ethical considerations in the deployment of federated learning
models, providing a comprehensive view of this rapidly evolving
field. Ultimately, our goal is to build a vibrant community that
propels federated learning into a pivotal role in addressing the
challenges and opportunities of Big Data analytics.<br>
<b><br>
Topics of interest include, but are not limited to, the
following:</b><br>
<br>
</p>
<ul>
<li>Federated learning algorithms for Big Data processing</li>
<li>Privacy-preserving mechanisms in federated learning</li>
<li>Security challenges and solutions in federated learning</li>
<li>Efficient model aggregation and optimization techniques</li>
<li>Applications of federated learning in healthcare, finance,
and IoT</li>
<li>Data governance and compliance in federated learning systems</li>
<li>Challenges and solutions for model updates in non-IID data
distributions</li>
<li>Resource-efficient federated learning for edge devices</li>
<li>Collaborative learning frameworks for multi-institutional
Big Data analytics</li>
<li>Evaluation metrics and benchmarking for federated learning
systems</li>
<li>Novel architectures and platforms for federated learning
deployment</li>
<li>Adaptive and personalized federated learning models</li>
<li>Federated Unlearning methodologies</li>
</ul>
<p><br>
<b>Important Dates</b></p>
<ul>
<li>Full paper submission: Sept 30, 2026</li>
<li>Notification of paper acceptance: Oct 31, 2026</li>
<li>Camera-ready of accepted papers: Nov 14, 2026</li>
<li>Conference: Dec 14-17, 2026</li>
</ul>
<p><b>Kind Regards</b><br>
</p>
<pre class="moz-signature" cols="72">--
Prof. Francesco Piccialli, Ph.D.
DMA - Department of Mathematics and Applications "R. Caccioppoli"
University of Naples Federico II, Italy
Tel. +39 081675787
Head and Scientific Director of M.O.D.A.L group: <a
class="moz-txt-link-freetext" href="https://www.labdma.unina.it"
moz-do-not-send="true">https://www.labdma.unina.it</a>
Web: <a class="moz-txt-link-freetext"
href="http://wpage.unina.it/francesco.piccialli/"
moz-do-not-send="true">http://wpage.unina.it/francesco.piccialli/</a>
Google Scholar: <a class="moz-txt-link-freetext"
href="https://scholar.google.it/citations?user=CLNn_9gAAAAJ&hl=it"
moz-do-not-send="true">https://scholar.google.it/citations?user=CLNn_9gAAAAJ&hl=it</a>
Institutional web: <a class="moz-txt-link-freetext"
href="https://www.docenti.unina.it/francesco.piccialli"
moz-do-not-send="true">https://www.docenti.unina.it/francesco.piccialli</a></pre>
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