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The emergence of multi-sensor Internet of Things (IoT) deployments has led to an immediate demand to have unified intelligence models that can take advantage of heterogeneous data modalities, such as time-series sensor readings, visual data, audio streams as well as contextual metadata, to form incoherent understanding...
The ubiquitous use of Internet of Things (IoT) system in dynamic and real-life contexts presents a high level of challenges because of the constant changes in the data distributions, otherwise known as concept drift. The conventional machine learning models that are implemented in IoT systems are usually trained in sta...
The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe...
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