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<p>Dear colleagues,</p>
<p>We are organizing a Special Session on "Towards Robust Federated
Learning: Addressing Data and Device Heterogeneity" at the
International Joint Conference on Neural Networks (IJCNN 2025, <a
class="moz-txt-link-freetext" href="https://2025.ijcnn.org/">https://2025.ijcnn.org/</a>),
to be held in Rome, Italy, from 30 June to 5 July 2025. </p>
<p>We would appreciate if you forward this CfP to friends and
colleagues that might be interested.</p>
<p>///////////////////////////////////////////////////////////</p>
<p><b>SPECIAL SESSION on Towards Robust Federated Learning:
Addressing Data and Device Heterogeneity</b></p>
<p>Federated Learning (FL) has emerged as a transformative framework
for enabling AI while respecting privacy and complying with
regulations. <br>
However, addressing non-iid data distributions and device
heterogeneity remains crucial for achieving scalable and effective
FL applications. <br>
This special session will bring together researchers and
practitioners to explore innovative solutions and advance the
field towards real-world deployments.<br>
<br>
Topics of Interest:<br>
We encourage submissions on topics including, but not limited to:<br>
<br>
- Algorithmic advances for non-iid data and heterogeneous devices<br>
- Fairness and robustness in FL<br>
- Interpretability and explainability in FL models<br>
- Personalization and client adaptivity<br>
- Resource-aware FL frameworks<br>
- Real-world applications in healthcare, IoT, and beyond<br>
- Tools, benchmarks, and evaluation metrics</p>
<p>=== ORGANIZERS ===</p>
Prof. Francesco Piccialli<br>
Mathematical mOdelling and Data AnaLysis (M.O.D.A.L.) -
<a class="moz-txt-link-freetext" href="https://www.labdma.unina.it">https://www.labdma.unina.it</a><br>
University of Naples Federico II, Italy<br>
<p>Dr. Diletta Chiaro<br>
University of Naples Federico II, Italy<br>
</p>
<p>Dr. Fabio Giampaolo<br>
University of Naples Federico II, Italy</p>
<p></p>
<p>=== SUBMISSION GUIDELINE ===</p>
<p>Prospective authors are invited to submit complete papers of no
more than eight (8) pages in the IEEE two-column conference
proceedings format. Please follow the submission guideline from
the IJCNN 2025 submission website.</p>
<p>Special session papers are treated the same as regular conference
papers. Please specify that your paper is for the Special Session
on Advances in Deep Learning for Biomedical Data Analysis.</p>
<p>All the accepted and presented papers will be published on IEEE
Xplore Digital Library and indexed by Scopus.</p>
<p>=== AUTHORS INFORMATIONS ===</p>
- Author instructions: <a class="moz-txt-link-freetext"
href="https://2025.ijcnn.org/authors/initial-author-instructions">https://2025.ijcnn.org/authors/initial-author-instructions</a><br>
- Paper submission guidelines: <a class="moz-txt-link-freetext"
href="https://cmt3.research.microsoft.com/IJCNN2025/">https://cmt3.research.microsoft.com/IJCNN2025/</a><br>
- Further information is available at: <a
class="moz-txt-link-freetext" href="https://2025.ijcnn.org/">https://2025.ijcnn.org/</a>
<p>=== CONTACT ===</p>
<p>Any inquiries can be directed to Francesco Piccialli:
<a class="moz-txt-link-abbreviated" href="mailto:francesco.piccialli@unina.it">francesco.piccialli@unina.it</a><br>
</p>
<p>///////////////////////////////////////////////////////////</p>
Best regards,<br>
Prof. Francesco Piccialli<br>
<p></p>
<p></p>
<p></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">https://www.labdma.unina.it</a>
Web: <a class="moz-txt-link-freetext" href="http://wpage.unina.it/francesco.piccialli/">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">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">https://www.docenti.unina.it/francesco.piccialli</a></pre>
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