Información de la conferencia

iEDGE 2025: International Symposium on Edge intelligence, Trustworthy and Decentralized Artificial Intelligence

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iEDGE
Día de Entrega:
2025-08-01
Fecha de Notificación:
2025-08-30
Fecha de conferencia:
2025-10-14
Ubicación:
Dubrovnik, Croatia
Vistas: 4616   Seguidores: 0   Asistentes: 0

Solicitud de Artículos

iEDGE 2025 (International Symposium on Edge intelligence, Trustworthy and Decentralized Artificial Intelligence) is an academic conference held in Dubrovnik, Croatia on 2025-10-14. The paper submission deadline is 2025-08-01. Acceptance notifications are sent on 2025-08-30.

iEDGE 2025 is a premier international venue dedicated to advancing research and innovation at the intersection of trustworthy, decentralized, and distributed AI systems. As the scale, complexity, and societal impact of AI systems grow, ensuring trust, privacy, robustness, and decentralization becomes critical. We invite researchers, practitioners, and innovators from academia and industry to join us in exploring the new frontiers of Decentralized Artificial Intelligence, Trustworthy Machine Learning, and Secure Distributed Systems. Co-located with FLTA 2025, iEDGE focuses on scaling trustworthiness principles beyond federated learning into the broader decentralized AI ecosystem. The International Symposium on Edge intelligence, Trustworthy and Decentralized Artificial Intelligence (iEDGE 2025) addresses the use of advanced intelligent systems in providing trustworthy decentralized AI solutions in many fields, and the challenges, approaches, and future directions. We invite the submission of original papers on all topics related to trustworthy and decentralized artificial intelligence, with special interest in but not limited to: Topics of Interest Decentralized Artificial Intelligence Decentralized foundation model training and serving Peer-to-peer (P2P) inference networks Blockchain-based machine learning platforms Distributed optimization for large AI models Incentive mechanisms in decentralized AI systems Trustworthy AI Systems Bias mitigation, fairness, and accountability Verifiable decentralized model updates Explainability in distributed learning Robustness against adversarial attacks Security and Privacy Secure aggregation and differential privacy Trust mechanisms for model collaboration Resilient learning under malicious participants Secure Edge AI deployments Edge Intelligence Edge deployment of open foundation models Collaborative edge-cloud AI systems Privacy-preserving AI for constrained environments 5G/6G architectures for decentralized AI Resource allocation and scheduling
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