For fully exploiting Deep Learning solutions in AIoT environments setting restrictions in processing power, memory consumption, hard real-timeness, handling uncertainties in the processing outcomes, and requiring a level of interpretability, a number of challenges need to be addressed through theoretical and methodological contributions, including but not limited to:
Efficient Deep Learning methodologies for Internet of Things
Deep Learning methodologies for distributed inference and learning
Continual Learning methods training lightweight Deep Learning models
Continual Inference of Deep Learning models
Lightweight Deep Learning models for visual, audio, sensor data analysis
Deep Learning models for efficient multimodal data analysis and fusion
Sensor time-series analysis based on Deep Learning
Deep Learning methodologies for smart cities, including Federated Learning, Transfer Learning, Domain Adaptation, and Split Computing
Privacy-preserving distributed learning and inference
Resource-efficient distributed learning
The aim of this special session is to bring together and disseminate state-of-the-art research contributions that address Distributed and Continual Inference and Learning approaches, including the analysis and design of novel algorithms and methodologies, innovative applications and enabling technologies, etc. Please consider to submit your latest research in the topic.
Submitted papers need to follow the guidelines of regular papers for IEEE MLSP 2024. Papers need to be submitted following the submission guidelines of IEEE MLSP 2024 and select the title of the Special Session during the submission process. Accepted papers will be included in the IEEE MLSP 2024 proceedings.
The Special Session is supported by the NordForsk Nordic University Cooperation on Edge Intelligence (NUEI) with project number 168043, and the HEU project PANDORA (GA number 101135775) .
For inquiries concerning this Special Session please feel free to contact us at ai [at] ece.au.dk