{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:41:19Z","timestamp":1783183279965,"version":"3.54.6"},"reference-count":18,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,2]]},"DOI":"10.23919\/cnsm50824.2020.9269105","type":"proceedings-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T21:36:55Z","timestamp":1606772215000},"page":"1-5","source":"Crossref","is-referenced-by-count":10,"title":["Building and Evaluating Federated Models for Edge Computing"],"prefix":"10.23919","author":[{"given":"Yasaman","family":"Amannejad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Federated ai technology enabler","author":"authors","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412771"},{"key":"ref12","article-title":"A generic framework for privacy preserving deep learning","author":"ryffel","year":"2018"},{"key":"ref13","article-title":"Leaf: A benchmark for federated settings","author":"caldas","year":"2018"},{"key":"ref14","article-title":"Tensorflow federated learning","year":"0"},{"key":"ref15","article-title":"Mnist dataset","author":"lecun","year":"0"},{"key":"ref16","article-title":"Federated learning tools","author":"amannejad","year":"2020"},{"key":"ref17","article-title":"MNIST handwritten digit database","author":"lecun","year":"2010"},{"key":"ref18","article-title":"Analysis of federated learning as a distributed solution for learning on edge devices","author":"lameh","year":"0","journal-title":"The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2020)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647927"},{"key":"ref3","article-title":"Privacy-preserving traffic flow prediction: A federated learning approach","author":"liu","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"ref6","first-page":"756","article-title":"D&#x00EF;ot: A federated self-learning anomaly detection system for iot","author":"nguyen","year":"2019","journal-title":"IEEE 39th International Conference on Distributed Computing Systems (ICDCS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"ref8","article-title":"Nvidia clara","author":"authors","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700332"},{"key":"ref2","article-title":"A federated learning approach for mobile packet classification","author":"bakopoulou","year":"2019"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref9","article-title":"Paddlefl","author":"authors","year":"2019"}],"event":{"name":"2020 16th International Conference on Network and Service Management (CNSM)","location":"Izmir, Turkey","start":{"date-parts":[[2020,11,2]]},"end":{"date-parts":[[2020,11,6]]}},"container-title":["2020 16th International Conference on Network and Service Management (CNSM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9269033\/9269034\/09269105.pdf?arnumber=9269105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T11:36:05Z","timestamp":1643196965000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9269105\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":18,"URL":"https:\/\/doi.org\/10.23919\/cnsm50824.2020.9269105","relation":{},"subject":[],"published":{"date-parts":[[2020,11,2]]}}}