The 2nd IEEE International Conference on Federated Learning Technologies and Applications (FLTA24)
About About The Conference
The 2nd International Conference on Federated Learning Technologies and Applications (FLTA 2024) is a premier venue for the timely publication of FL management, systems, services, technologies, and applications. FLTA 2024 aims to provide attendees with a comprehensive understanding of FL communication, computing, and system requirements. Through keynote speeches, panel discussions, and presentations, attendees can engage with leading experts and learn about the latest developments and future trends in the field.
FLTA 2024 focuses on fostering an understanding of FL, identifying technical challenges, and exploring potential solutions, including distributed optimization, privacy-preserving techniques, intelligent learning algorithms, personalized FL, communication efficiency approaches, Secure and Trustworthy FL, open challenges, and recent trends and opportunities in FL. We welcome submissions addressing the important challenges (see the topics of interest>> link it with the TOPICS OF INTEREST on the home page ) and presenting novel research or experimentation results with system or network-related case studies. Survey papers that offer a perspective on related work and identify key challenges for future research will be considered as well.
About FULL PAPER IMPORTANT DATES
Full Paper Submission: | July 7, 2024 Final and Firm |
Paper Acceptance Notification: | July 31, 2024
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Camera Ready Submission: | August 14, 2024 |
WORKSHOPS & SPECIAL SESSIONS (WSS) IMPORTANT DATES | |
WSS Proposal Submission | April 30, 2024 |
Workshop Notification | May 15, 2024 |
WSS Paper Submission | July 1, 2024 |
Paper Notification | August 1, 2024 |
Camera Ready Submission: | August 14, 2024 |
Conference VALENCIA, SPAIN 17-19 September 2024 |
All papers accepted, registered, and presented in FLTA and the co-located workshops will be submitted to IEEEXplore for publication.
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ScopeScope
- Federated learning frameworks
- Federated learning architectures
- Federated learning algorithms
- Privacy-preserving Federated Learning
- Federated Learning communication efficiency
- Distributed Machine Learning
Topics of interest:Topics of interest:
- Federated Learning frameworks
- Federated Learning Aggregation Algorithms
- Federated Learning Applications
- Federated Learning Deployment Architectures
- Privacy-Preserving FL Techniques
- Federated Learning Communication-efficiency
- Federated Learning modelling and simulation tools
- Federated Learning datasets and benchmarking
- Federated Learning Associated Technologies