RESTART patronises the Second International Workshop on the Integration between Distributed Machine Learning and the Internet of Things | 14 October 2024
The Second International Workshop on the Integration between Distributed Machine Learning and the Internet of Things will be held on the 14 October 2024 in Athens, Greece, under the auspices of the RESTART research and development programme.
Nowadays, the impressive proliferation of IoT devices (predicted to reach 30 billion by 2030), able to monitor several real-world processes and environments, is driving the development of extreme analytics for business decisions based on the vast amount of data collected by smart objects. Indeed, emerging wireless technologies, such as 5G and LPWAN, are enabling the possibility to easily and efficiently connect tiny devices, which are also equipped with heterogeneous computational capacity, varying from smartphones to micro-controllers, deployed over large geographical areas.
In such a context, emerging learning mechanisms, such as distributed and federated learning, can be a promising alternative to traditional centralized analytics.
The workshop AIoT 2024 is specifically meant to gather new ideas, contributions, and experiences on the integration of Distributed and Federated Machine Learning with long-range IoT systems. The workshop solicits original papers dealing with the open challenges in the integration between Distributed/Federated Learning and IoT, including theoretical works and practical experiences over emulated and/or real testbeds. Contributions on the optimization of Machine and Deep Learning over embedded IoT devices are also welcome.
Topics include, but are not limited to:
- Efficient Machine Learning in the IoT
- Hardware for Machine Learning and Deep Learning in the IoT
- Network Layer technologies to support Machine Learning in the IoT
- Protocols to support Distributed Machine Learning in the IoT
- Edge computing and IoT for distributed/federated learning
- Experimental validation of distributed machine learning for IoT
- Testbeds and tools for distributed machine learning in IoT
- Privacy-preserving data sharing and aggregation in distributed/federated learning
- Datasets and applications of distributed/federated learning in IoT (eg. spectrum sensing, healthcare, smart cities, and transportation)
- Scalability and performance issues in IoT and distributed/federated learning
- Emerging trends, challenges, and future directions in IoT and distributed/federated learning
For further information, visit the website.