{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T06:11:05Z","timestamp":1758089465452,"version":"3.44.0"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,22]]},"DOI":"10.1109\/dac63849.2025.11133269","type":"proceedings-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T17:35:41Z","timestamp":1757957741000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["Resilient Federated Learning on Embedded Devices with Constrained Network Connectivity"],"prefix":"10.1109","author":[{"given":"Zihan","family":"Li","sequence":"first","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ao","family":"Li","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Hsiang","family":"Chan","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yevgeniy","family":"Vorobeychik","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Yeoh","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Lou","sequence":"additional","affiliation":[{"name":"Virginia Polytechnic Institute and State University,VA,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Washington University,St. Louis,MO,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3564652"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3501813"},{"key":"ref3","article-title":"Sok: Security and privacy risks of medical ai","author":"Chang","year":"2024","journal-title":"arXiv preprint arXiv:2409.07415"},{"key":"ref4","first-page":"2024","article-title":"Federated learning meets blockchain","author":"Morgan","year":"2024"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0009"},{"key":"ref6","first-page":"20675","article-title":"Self-aware personalized federated learning","volume":"35","author":"Chen","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SP54263.2024.00120"},{"key":"ref8","first-page":"29995","article-title":"Delayed gradient averaging: Tolerate the communication latency for federated learning","volume":"34","author":"Zhu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","first-page":"41399","article-title":"No one idles: Efficient heterogeneous federated learning with parallel edge and server computation","volume-title":"International Conference on Machine Learning","author":"Zhang"},{"key":"ref10","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"Lin","year":"2017","journal-title":"arXiv preprint arXiv:1712.01887"},{"key":"ref11","article-title":"Qsgd: Communication-efficient sgd via gradient quantization and encoding","volume":"30","author":"Alistarh","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294915"},{"key":"ref14","article-title":"Gradient sparsification for communication-efficient distributed optimization","volume":"31","author":"Wangni","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref15","first-page":"61556165","article-title":"Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression","volume-title":"International Conference on Machine Learning","author":"Tang"},{"key":"ref16","article-title":"Resolving the tug-of-war: a separation of communication and learning in federated learning","volume":"36","author":"Li","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01566"},{"key":"ref18","first-page":"2303423054","article-title":"Progfed: Effective, communication, and computation efficient federated learning by progressive training","volume-title":"in International Conference on Machine Learning","author":"Wang"},{"key":"ref19","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"in Artificial intelligence and statistics"},{"key":"ref20","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li"},{"key":"ref21","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International conference on machine learning","author":"Karimireddy"},{"key":"ref22","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv preprint arXiv:1903.03934"},{"issue":"110","key":"ref23","first-page":"143","article-title":"A general theory for federated optimization with asynchronous and heterogeneous clients updates","volume":"24","author":"Fraboni","year":"2023","journal-title":"Journal of Machine Learning Research"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3532577.3532591"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref29","article-title":"On the convergence of fedavg on non-iid data","author":"Li","year":"2019","journal-title":"arXiv preprint arXiv:1907.02189"},{"article-title":"Efficient and robust asynchronous federated learning with stragglers","volume-title":"International Conference on Learning Representations","author":"Chen","key":"ref30"},{"key":"ref31","first-page":"20531","article-title":"Simigrad: Fine-grained adaptive batching for large scale training using gradient similarity measurement","volume":"34","author":"Qin","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567991"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3516567"},{"key":"ref34","article-title":"Adaptive federated optimization","author":"Reddi","year":"2020","journal-title":"arXiv preprint arXiv:2003.00295"},{"key":"ref35","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Nguyen"},{"key":"ref36","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv preprint arXiv:1409.1556"}],"event":{"name":"2025 62nd ACM\/IEEE Design Automation Conference (DAC)","start":{"date-parts":[[2025,6,22]]},"location":"San Francisco, CA, USA","end":{"date-parts":[[2025,6,25]]}},"container-title":["2025 62nd ACM\/IEEE Design Automation Conference (DAC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11132383\/11132091\/11133269.pdf?arnumber=11133269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:32:17Z","timestamp":1758000737000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11133269\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/dac63849.2025.11133269","relation":{},"subject":[],"published":{"date-parts":[[2025,6,22]]}}}