AIoT, the Third International ACM MobiHoc Workshop on the Integration between Distributed Machine Learning and the Internet of Things, aims to bring together researchers and practitioners from academia and industry to explore the design, deployment, and operation of distributed intelligence in resource-constrained and large-scale IoT systems. We invite original contributions in the form of theoretical insights, algorithmic advances, experimental evaluations, and real-world applications. Topics of interest include, but are not limited to:

· Efficient Machine Learning on low-power or constrained IoT systems
· Distributed, Federated, and Split Learning across edge and cloud systems in IoT environments
· System architectures and runtime optimization for learning in IoT systems
· Hardware acceleration and platform co-design for edge intelligence in IoT systems
· Communication and networking support for distributed model training in IoT systems
· Protocols for model sharing, updates, and coordination in IoT systems
· Edge collaboration and cross-device intelligence in IoT systems
· Privacy-preserving training methods and secure aggregation mechanisms for distributed, Federated, and Edge Learning in IoT systems
· Experimental testbeds, real-world deployments, and benchmarking tools for IoT systems
· Applications in areas such as smart cities, healthcare, industrial IoT systems, agriculture, and transportation
· Scalability, reliability, and performance tuning for large-scale IoT systems
· Open challenges, new directions, and emerging trends in decentralized learning for IoT systems
· Model personalization and adaptation techniques for Federated Learning in IoT systems
· Fault tolerance, robustness, and reliability in Distributed Learning for IoT systems
· Edge AI for low-latency applications in IoT systems
· Energy-aware learning algorithms for IoT systems
· Cross-platform Machine Learning for heterogeneous IoT systems
· Network slicing and QoS-aware techniques for Federated Learning in IoT systems
· Evolutionary models and online learning techniques in IoT systems
· Decentralized consensus algorithms for model coordination in IoT systems
· AI techniques for IoT security