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