International Conference on Federated Learning and Data Science
(ICFLDS - 26)

1st - 2nd October 2026 Montreal, Canada (Hybrid Event)

Call for Paper

The ICFLDS is committed to addressing global challenges through impactful research and sustainable solutions. It brings together researchers dedicated to advancing knowledge for societal benefit. Focusing on Artificial Intelligence,Data Science,Machine Learning, the conference promotes research aligned with global development goals and long-term sustainability. Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Federated learning for privacy-preserving AI
  • Challenges in federated learning implementation
  • Applications of federated learning in healthcare
  • Data sharing in federated learning systems
  • Federated learning for edge computing
  • Ethical considerations in federated learning
  • Federated learning in financial services
  • Real-world case studies of federated learning
  • Federated learning for IoT devices
  • Performance evaluation of federated learning models
  • Collaborative learning without data centralization
  • Federated learning in mobile applications
  • Data security in federated learning frameworks
  • Future trends in federated learning research
  • Federated learning for natural language processing
  • Integrating federated learning with blockchain
  • Federated learning for personalized AI models
  • Scalability issues in federated learning systems
  • Federated learning in smart cities
  • Impact of federated learning on data ownership
Review & Publication

All submissions will be reviewed for their contribution to global impact and research quality. Accepted papers will be presented and considered for publication in reputed platforms.

Registration

Join participants from around the world by completing your registration and becoming part of a global research community.

Publication

Accepted papers will gain international exposure through conference presentations and publication opportunities.

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