Connectionists: Call For Papers - IEEE BIGDATA 2025 - 2nd Special Session on Federated Learning on Big Data

[Francesco Piccialli]

*IEEE BIGDATA 2025 - 2nd Special Session on Federated Learning on Big Data
*

*Organizers*

Prof. Francesco Piccialli, University of Naples Federico II, Italy
Dr. Fabio Giampaolo, University of Naples Federico II, Italy
Prof. David Camacho, Universidad Politecnica de Matrid, Spain
Prof. Antonella Guzzo, University of Calabria, Italy

*Official Link:* 
https://conferences.cis.um.edu.mo/ieeebigdata2025/special_sessions.html#federated

*Aim and Scope*

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.

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.

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.
*
Topics of interest include, but are not limited to, the following:*

  * Federated learning algorithms for Big Data processing
  * Privacy-preserving mechanisms in federated learning
  * Security challenges and solutions in federated learning
  * Efficient model aggregation and optimization techniques
  * Applications of federated learning in healthcare, finance, and IoT
  * Data governance and compliance in federated learning systems
  * Challenges and solutions for model updates in non-IID data distributions
  * Resource-efficient federated learning for edge devices
  * Collaborative learning frameworks for multi-institutional Big Data
    analytics
  * Evaluation metrics and benchmarking for federated learning systems
  * Novel architectures and platforms for federated learning deployment
  * Adaptive and personalized federated learning models
  * Federated Unlearning methodologies


*Important Dates*

  * Full paper submission: Sept 29, 2025
  * Notification of paper acceptance: Oct 31, 2025
  * Camera-ready of accepted papers: Nov 14, 2025
  * Conference: Dec 8-11, 2025

*Kind Regards*

-- 
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:https://www.labdma.unina.it
Web:http://wpage.unina.it/francesco.piccialli/
Google Scholar:https://scholar.google.it/citations?user=CLNn_9gAAAAJ&hl=it
Institutional web:https://www.docenti.unina.it/francesco.piccialli
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