AIChallengeIoT 2020 - 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things

Artificial intelligence (AI) and machine learning (ML) are key enabling technologies for many Internet of Things (IoT) applications. However, the collection and processing of data for AI and ML is very challenging in the IoT domain. For example, there are usually a large number of low-powered sensors deployed in large geographical areas with possibly intermittent network connectivity. The sensors and their collected data may be owned by different users or organizations, which can bring further obstacles to data collection due to privacy concerns and noisy labels provided by different users. The successful application of AI/ML approaches in such scenarios with noisy and decentralized data is difficult. In addition, the amount of collected data that can be used for training AI/ML models is usually proportional to the number of users in the system, but the system may not be able to attract many users without a well-trained AI/ML model, and it is challenging to solve this dilemma.
This workshop focuses on how to address the above and other unique challenges of applying AI/ML in IoT systems.
We invite researchers and practitioners to submit papers describing original work, experiences, or vision related to the entire lifecycle of an IoT system powered by AI and ML, including (but not limited to) the following topics:
AI/ML in multi-agent, distributed, and decentralized settings
AI/ML on low-powered and/or intermittently connected devices
AI/ML with noisy and possibly adversarial data and labels
Algorithms and techniques for evolving from a new system that is initially trained with only a small amount of data
Algorithms and techniques for making use of data collected by geographically dispersed sensors to provide useful services through AI/ML
Algorithms and techniques for reducing human effort in data labeling, including active learning
Algorithms and techniques for sharing data and training AI/ML models while preserving user sensitive information, including federated learning
Design and implementation of AI/ML-powered IoT systems
Hardware, software, and tools for AI/ML in IoT
IoT applications enabled by AI/ML
Privacy and security of AI/ML in IoT