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Found 5 papers, 1 papers with code
Cheating off your neighbors: Improving activity recognition through corroboration
From solely an individual's perspective, it can be difficult to differentiate between these activities as they may appear very similar, even though they are markedly different.
Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models
We implement and evaluate Sasha in a hands-on user study, showing the capabilities and limitations of LLM-driven smart homes when faced with unconstrained user-generated scenarios.
iDML: Incentivized Decentralized Machine Learning
We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.
"Get ready for a party": Exploring smarter smart spaces with help from large language models
We first explore the feasibility of a system that places an LLM at the center of command inference and action planning, showing that LLMs have the capacity to infer intent behind vague, context-dependent commands like "get ready for a party" and respond with concrete, machine-parseable instructions that can be used to control smart devices.
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications
We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences.
