Hybrid Conferencee

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

4th - 5th September 2026 | Medan, Indonesia

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Conference Notifications:

"Be sure to check this section regularly for all Research Plus International Conference updates. We’ll keep you informed about deadlines, event details, and more important notifications."

Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Medan. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Medan event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Medan conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Medan, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Medan."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICFLDS aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Artificial Intelligence,Data Science,Machine Learning, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Federated learning for privacy-preserving AI
02
Challenges in federated learning implementation
03
Applications of federated learning in healthcare
04
Data sharing in federated learning systems
05
Federated learning for edge computing
06
Ethical considerations in federated learning
07
Federated learning in financial services
08
Real-world case studies of federated learning
09
Federated learning for IoT devices
10
Performance evaluation of federated learning models
11
Collaborative learning without data centralization
12
Federated learning in mobile applications
13
Data security in federated learning frameworks
14
Future trends in federated learning research
15
Federated learning for natural language processing
16
Integrating federated learning with blockchain
17
Federated learning for personalized AI models
18
Scalability issues in federated learning systems
19
Federated learning in smart cities
20
Impact of federated learning on data ownership