{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-federated-learning-in-resource","title":"Adaptive Federated Learning in Resource Constrained Edge Computing Systems","arxiv_id":"1804.05271","date":"2018-04-14","proceeding":null,"authors":["Shiqiang Wang","Tiffany Tuor","Theodoros Salonidis","Kin K. Leung","Christian Makaya","Ting He","Kevin Chan"],"abstract":"Emerging technologies and applications including Internet of Things (IoT),\nsocial networking, and crowd-sourcing generate large amounts of data at the\nnetwork edge. Machine learning models are often built from the collected data,\nto enable the detection, classification, and prediction of future events. Due\nto bandwidth, storage, and privacy concerns, it is often impractical to send\nall the data to a centralized location. In this paper, we consider the problem\nof learning model parameters from data distributed across multiple edge nodes,\nwithout sending raw data to a centralized place. Our focus is on a generic\nclass of machine learning models that are trained using gradient-descent based\napproaches. We analyze the convergence bound of distributed gradient descent\nfrom a theoretical point of view, based on which we propose a control algorithm\nthat determines the best trade-off between local update and global parameter\naggregation to minimize the loss function under a given resource budget. The\nperformance of the proposed algorithm is evaluated via extensive experiments\nwith real datasets, both on a networked prototype system and in a larger-scale\nsimulated environment. The experimentation results show that our proposed\napproach performs near to the optimum with various machine learning models and\ndifferent data distributions.","url_abs":"http://arxiv.org/abs/1804.05271v3","url_pdf":"http://arxiv.org/pdf/1804.05271v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-federated-learning-in-resource","repo_url":"https://github.com/IBM/adaptive-federated-learning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"edge-computing","task_name":"Edge-computing"},{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.05271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05271"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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