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International Conference on Federated Learning and Data Science

ICFLDS

1st Feb – 2nd Feb 2027 Vienna, Austria

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

Connect with global educators & researchers

Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

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Terms & Condition

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Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 9 SDG 11 SDG 16 SDG 17
01 Advancements in Federated Learning Algorithms +
This track focuses on the latest developments in federated learning algorithms that enhance model accuracy and efficiency. Researchers are invited to present novel approaches that address the challenges of decentralized data processing.
SDG 9 SDG 11
02 Privacy-Preserving Techniques in AI +
This session explores innovative privacy-preserving methodologies within artificial intelligence frameworks. Contributions should highlight techniques that safeguard user data while maintaining model performance.
SDG 16
03 Distributed Machine Learning Architectures +
This track examines the architectural designs that facilitate distributed machine learning across various platforms. Papers should discuss scalability, robustness, and the integration of edge computing.
SDG 9 SDG 11
04 Secure Multi-Party Computation in Data Science +
This session delves into secure multi-party computation techniques that enable collaborative data analysis without compromising privacy. Researchers are encouraged to share insights on practical applications and theoretical advancements.
SDG 16
05 Collaborative Model Training Strategies +
This track focuses on strategies for collaborative model training that leverage decentralized data sources. Submissions should address challenges and solutions in synchronizing model updates across diverse environments.
SDG 17
06 Edge AI and Its Applications +
This session highlights the role of edge AI in enhancing federated learning processes. Contributions should explore real-world applications and the implications of deploying AI models on mobile and edge devices.
SDG 9 SDG 11
07 Differential Privacy in Federated Learning +
This track investigates the integration of differential privacy techniques within federated learning frameworks. Papers should focus on balancing privacy guarantees with model utility and performance.
SDG 16
08 Cross-Device Learning Paradigms +
This session addresses the unique challenges and solutions associated with cross-device learning in federated settings. Researchers are invited to present methodologies that optimize learning across heterogeneous devices.
SDG 9 SDG 16
09 Data Sovereignty and Federated Learning +
This track explores the implications of data sovereignty on federated learning practices. Contributions should discuss regulatory considerations and their impact on model training and deployment.
SDG 16
10 Communication-Efficient Learning Techniques +
This session focuses on techniques that enhance communication efficiency in federated learning environments. Papers should present innovative methods to reduce bandwidth usage while ensuring model convergence.
SDG 9 SDG 11
11 Federated Optimization Methods +
This track examines optimization strategies specifically designed for federated learning scenarios. Researchers are encouraged to share novel algorithms that improve convergence rates and overall model performance.
SDG 9 SDG 11