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International Conference on Federated Learning and Data Science - (ICFLDS-27)

28th - 29th June 2027 , Surat Thani - Thailand

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Call For Papers

The (ICFLDS-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Artificial Intelligence, Data Science, Machine Learning are explored to promote cross-domain knowledge exchange and collaborative innovation.

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

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.

Submit Your Research Work Register Now