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Found 24 papers, 12 papers with code
LlamaTouch: A Faithful and Scalable Testbed for Mobile UI Automation Task Evaluation
The emergent large language/multimodal models facilitate the evolution of mobile agents, especially in the task of mobile UI automation.
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models.
A First Look at GPT Apps: Landscape and Vulnerability
With the advancement of Large Language Models (LLMs), increasingly sophisticated and powerful GPTs are entering the market.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
LLMCad: Fast and Scalable On-device Large Language Model Inference
Currently, the execution of these generative tasks heavily depends on Large Language Models (LLMs).
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks.
Mobile Foundation Model as Firmware
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
FwdLLM: Efficient FedLLM using Forward Gradient
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM.
Uncertain Machine Ethical Decisions Using Hypothetical Retrospection
We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Ove Hansson to improve existing approaches to machine ethical reasoning by accounting for probability and uncertainty from a position of Philosophy that resonates with humans.
Federated Few-Shot Learning for Mobile NLP
Such an inadequacy of data labels is known as a few-shot scenario; it becomes the key blocker for mobile NLP applications.
Towards Practical Few-shot Federated NLP
To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting.
FedAdapter: Efficient Federated Learning for Modern NLP
A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive.
Benchmarking of DL Libraries and Models on Mobile Devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
Boosting Mobile CNN Inference through Semantic Memory
Human brains are known to be capable of speeding up visual recognition of repeatedly presented objects through faster memory encoding and accessing procedures on activated neurons.
Hierarchical Federated Learning through LAN-WAN Orchestration
Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN.
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings.
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data.
Approximate Query Service on Autonomous IoT Cameras
Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts.
Databases
Video Analytics with Zero-streaming Cameras
For efficiency, we advocate for these cameras to be zero streaming: capturing videos to local storage and communicating with the cloud only when analytics is requested.
A First Look at Deep Learning Apps on Smartphones
We are in the dawn of deep learning explosion for smartphones.
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks.
DeepCache: Principled Cache for Mobile Deep Vision
We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision.
