The Third International Workshop on the Integration between Distributed Machine Learning and the Internet of Things (AIoT)

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The rapid growth of connected devices (expected to exceed 30 billion by 2030) is reshaping how we collect and process information from the physical world. Devices ranging from smartphones and wearables to industrial sensors and microcontrollers are continuously generating vast amounts of data. Meanwhile, advanced wireless technologies such as 5G, Wi-Fi 6/7, and LPWAN are making it possible to connect a wide range of devices across large and diverse environments.

In this setting, emerging approaches like Distributed, Federated, and Edge Learning are gaining momentum. These methods bring intelligence closer to the data, reducing communication overhead, enabling faster decisions, improving privacy, and supporting energy-efficient processing. Such techniques are particularly relevant in IoT infrastructures that blend local sensing, computation, and actuation, encompassing not only traditional deployments but also edge-cloud infrastructures, cyber-physical systems, and collaborative networks of autonomous agents.

At the same time, integrating Distributed Learning into these environments introduces several open challenges. These include compressing models for transmission over constrained or unreliable networks, managing limited and heterogeneous resources at the edge, accelerating training under dynamic conditions, and ensuring security and privacy when data remains locally distributed.

The Third International Workshop on the Integration between Distributed Machine Learning and the Internet of Things (AIoT) serves as a venue for researchers and practitioners exploring how Distributed and Federated Learning can be applied in real-world, resource-constrained, and large-scale IoT networks. Topics span a wide range of connected setups, where decentralized intelligence plays a key role in enabling robust, adaptive, and efficient operations.

Specifically, the workshop broadly welcomes contributions spanning on-device intelligence, TinyML, Distributed/Federated/Split Learning, communication-efficient AI, and adaptive edge intelligence for heterogeneous IoT environments.

Topics of interest include, but are not limited to:

• Efficient Machine Learning on low-power or constrained IoT systems
• Continual, adaptive, and online learning techniques for dynamic IoT environments
• Distributed, Federated, and Split Learning across edge and cloud systems in IoT environments
• Foundation models, multimodal intelligence, and lightweight generative AI for IoT systems
• AI-native protocols and cross-layer co-design for intelligent IoT infrastructures
• 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
• TinyML, model compression, pruning, quantization, and inference optimization for distributed 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-driven techniques for IoT security

We welcome both theoretical contributions and applied work, including case studies and practical deployments. Our aim is to bring together a diverse community of experts working at the intersection of Machine Learning, networking, systems, and connected intelligence.