On-device Training: A First Overview on Existing Systems

• URL: http://arxiv.org/abs/2212.00824v3
• Date: Mon, 23 Sep 2024 07:59:38 GMT
• Title: On-device Training: A First Overview on Existing Systems
• Authors: Shuai Zhu, Thiemo Voigt, JeongGil Ko, Fatemeh Rahimian,
• Abstract summary: Efforts have been made to deploy some models on resource-constrained devices as well. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities.
• Score: 6.551096686706628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (ii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities and provide a survey of on-device training from a systems perspective.

Related papers

• On-Device Language Models: A Comprehensive Review [26.759861320845467]
  Review examines the challenges of deploying computationally expensive large language models on resource-constrained devices. Paper investigates on-device language models, their efficient architectures, as well as state-of-the-art compression techniques. Case studies of on-device language models from major mobile manufacturers demonstrate real-world applications and potential benefits.
  arXiv  Detail & Related papers  (2024-08-26T03:33:36Z)
• MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases [81.70591346986582]
  We introduce MobileAIBench, a benchmarking framework for evaluating Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices. MobileAIBench assesses models across different sizes, quantization levels, and tasks, measuring latency and resource consumption on real devices.
  arXiv  Detail & Related papers  (2024-06-12T22:58:12Z)
• Beimingwu: A Learnware Dock System [42.54363998206648]
  This paper describes Beimingwu, the first open-source learnware dock system providing foundational support for future research of learnware paradigm. The system significantly streamlines the model development for new user tasks, thanks to its integrated architecture and engine design. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security.
  arXiv  Detail & Related papers  (2024-01-24T09:27:51Z)
• A Survey of Serverless Machine Learning Model Inference [0.0]
  Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems.
  arXiv  Detail & Related papers  (2023-11-22T18:46:05Z)
• On Efficient Training of Large-Scale Deep Learning Models: A Literature Review [90.87691246153612]
  The field of deep learning has witnessed significant progress, particularly in computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications. With the increasing demands on computational capacity, a comprehensive summarization on acceleration techniques of training deep learning models is still much anticipated.
  arXiv  Detail & Related papers  (2023-04-07T11:13:23Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Challenges and Obstacles Towards Deploying Deep Learning Models on Mobile Devices [1.422288795020666]
  Deep learning models are developed using plethora of high-level, generic frameworks and libraries. Running those models on the mobile devices require hardware-aware optimizations. In this paper, we present the existing challenges, obstacles, and practical solutions towards deploying deep learning models on mobile devices.
  arXiv  Detail & Related papers  (2021-05-06T12:40:28Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations [8.647853543335662]
  We present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS) LIMITS applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification.
  arXiv  Detail & Related papers  (2020-01-28T06:34:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.