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An end-to-end mobile system for proactive stress monitoring from wearable signals supported by an extensible split federated learning system that enables on-device LoRA fine-tuning of large language models.
Physical-layer fingerprinting (PLF) has emerged as a promising authentication approach for Internet of Things devices. However, most PLF systems acquire and process a fixed-length waveform for every authentication query, burdening resource-constrained edge nodes. To address this issue, we propose a progressive-observat...
Mobile and wearable sensing enables health agents to provide personalized insights from users' health data, but existing cloud-hosted LLM backends raise privacy concerns. We present MobileRLHF, a privacy-preserving and self-improving framework that enables human-feedback-driven preference adaptation directly on commodi...
This paper investigates joint trajectory planning and computation offloading in multi-UAV assisted mobile edge computing (MEC) networks within complex 3D urban environments. Existing deep multi-agent reinforcement learning (MARL) methods often oversimplify obstacles as geometric no-fly zones, ignoring fine-grained chan...
L-shaped SFT is presented, a split fine-tuning framework that removes the need for continuous client participation and introduces one-shot SFT, in which clients upload activations once and then go offline while the server continues optimization over cached representations.
FlexP-SFT is proposed, a novel aggregation-free framework for personalized split federated fine-tuning, which fundamentally eliminates the client-side aggregation process and introduces a layer-flexible alignment strategy to balance personalization and generalization capabilities.
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