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🔥 PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while …
Advances in Neural Information Processing Systems September, 2026
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🔥 PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing …
Advances in Neural Information Processing Systems September, 2026
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Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training
Device-cloud collaboration holds promise for deploying large language models (LLMs), leveraging lightweight on-device models for …
International Conference on Machine Learning April, 2026
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Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training
Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs
Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused …
International Conference on Machine Learning April, 2026
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Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs
Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings
Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities …
ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) 🏆 Best Paper Runner-Up Award October, 2025
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Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings
LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
The rise of large language models (LLMs) has made natural language-driven route planning an emerging research area that encompasses …
Findings of the Association for Computational Linguistics: EMNLP 2025 August, 2025
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LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across …
IEEE International Conference on Communications (ICC) June, 2024
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FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
A Survey of Federated Learning for Connected and Automated Vehicles
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to …
26th IEEE International Conference on Intelligent Transportation Systems (ITSC) September, 2023
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A Survey of Federated Learning for Connected and Automated Vehicles
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the …
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops June, 2023
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Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
M²DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision …
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops June, 2023
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M²DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer