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{
    "pk": 62212,
    "title": "[SoK] Systematizing Inference Placement For Deep Learning Across Edge And Cloud Platforms: A Multi-Objective Optimization Perspective",
    "subtitle": null,
    "abstract": "<p>Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) models. To mitigate these challenges, researchers have proposed optimizing and offloading partitions of DL models among user devices, edge servers, and the cloud. In this setting, users can take advantage of different services to support their intelligent applications. For example, edge resources offer low response latency. In contrast, cloud platforms provide low monetary cost computation resources for computation-intensive workloads. However, communication between DL model partitions can introduce transmission bottlenecks and pose risks of data leakage. Recent research aims to balance accuracy, computation delay, transmission delay, and privacy concerns. They address these issues with model compression, model distillation, transmission compression, and model architecture adaptations, including internal classifiers. This survey contextualizes the state-of-the-art model offloading methods and model adaptation techniques by studying their implication to a multi-objective optimization comprising inference latency, data privacy, and resource monetary cost.</p>",
    "language": "eng",
    "license": {
        "name": "Creative Commons Attribution-NonCommercial  4.0",
        "short_name": "CC BY-NC 4.0",
        "text": "Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nNonCommercial — You may not use the material for commercial purposes.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.",
        "url": "https://creativecommons.org/licenses/by-nc/4.0"
    },
    "keywords": [],
    "section": "Articles",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/23j1s4bg",
    "frozenauthors": [
        {
            "first_name": "Zongshun",
            "middle_name": "",
            "last_name": "Zhang",
            "name_suffix": "",
            "institution": "Boston University",
            "department": "Department of Computer Science",
            "country": "United States"
        },
        {
            "first_name": "Ibrahim",
            "middle_name": "",
            "last_name": "Matta",
            "name_suffix": "",
            "institution": "Boston University",
            "department": "Department of Computer Science",
            "country": "United States"
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-12-30T18:45:00Z",
    "render_galley": {
        "label": "PDF",
        "type": "pdf",
        "path": "https://journalpub.escholarship.org/jsys/article/62212/galley/48046/download/"
    },
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            "label": "PDF",
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            "path": "https://journalpub.escholarship.org/jsys/article/62212/galley/48046/download/"
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}