Expanding the cloud-to-edge continuum to the IoT in serverless federated learning
Davide Loconte, Saverio Ieva, Agnese Pinto, Giuseppe Loseto, Floriano Scioscia, Michèle Ruta
Abstract
Serverless computing enables greater flexibility and efficiency in the cloud-to-edge continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly from this paradigm, as they need to gather, preprocess, aggregate and analyze data at various scales. In such contexts, the increasing hardware/software resource availability of Internet of Things (IoT) devices provides the opportunity to exploit them not only as data sources in AI/ML infrastructures, but also as computational nodes for model training and inference; nevertheless, comprehensive frameworks are still mostly missing. This work introduces an innovative serverless computing architecture which expands the cloud-to-edge continuum toward IoT devices. The same functions can run on IoT, edge and cloud nodes with minimal to no code modification and they can be invoked through a uniform interface. A federated learning framework is defined based on the proposed architecture, exploiting an existing IoT-oriented ML algorithm in a novel way. Notably, IoT nodes are used for both federated training and local inference tasks. A full prototype implementation has been built with off-the-shelf technologies and devices. A case study on federated machine learning for activity recognition and experiments have been conducted to validate key elements of the proposal.