An official invitation letter will be provided upon successful registration for your participation in the conference.
학술대회 참가 등록이 정상적으로 완료되면 공식 초청장이 발급됩니다.
Plenary, keynote and parallel sessions.
전체회의, 기조연설 및 분과 세션.
Connect with fellow researchers.
동료 연구자들과의 교류.
Digital certificate of participation.
디지털 참가 증명서 발급.
Official letter after successful registration.
등록 완료 후 공식 초청장 발급.
E-proceedings & resource materials.
전자 논문집 및 참고 자료.
Learn from leading experts & scholars.
저명한 전문가 및 학자들의 강연.
The conference's session tracks effectively support the following SDGs.
본 학술대회의 세션 트랙은 다음의 지속가능발전목표를 효과적으로 지원합니다.
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.
This session explores innovative privacy-preserving methodologies within artificial intelligence frameworks. Contributions should highlight techniques that safeguard user data while maintaining model performance.
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.
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.
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.
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.
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.
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.
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.
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.
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.