{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T10:35:02Z","timestamp":1786962902295,"version":"build-2736575974"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,9,6]]},"abstract":"<jats:p>Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independent and identically distributed (non-IID) across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater energy efficiency, and up to 2.06x speedup in convergence to target accuracy through fair distribution of the FL workload.<\/jats:p>","DOI":"10.1145\/3550289","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-29","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["FLAME"],"prefix":"10.1145","volume":"6","author":[{"given":"Hyunsung","family":"Cho","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akhil","family":"Mathur","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahim","family":"Kawsar","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proc. 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When the Curious Abandon Honesty: Federated Learning Is Not Private. arXiv preprint arXiv:2112.02918 (2021)."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-014-0506-3"},{"key":"e_1_2_2_7_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 (feb","author":"McMahan H. Brendan","year":"2017","unstructured":"H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 (feb 2017). arXiv:1602.05629 https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/1602.05629"},{"key":"e_1_2_2_8_1","unstructured":"Sebastian Caldas Jakub Kone\u010dny H. Brendan McMahan and Ameet Talwalkar. 2019. 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Model pruning enables efficient federated learning on edge devices. arXiv preprint arXiv:1909.12326 (2019)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Peter Kairouz H. Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings Rafael G. L. D'Oliveira Hubert Eichner Salim El Rouayheb David Evans Josh Gardner Zachary Garrett Adri\u00e0 Gasc\u00f3n Badih Ghazi Phillip B. Gibbons Marco Gruteser Zaid Harchaoui Chaoyang He Lie He Zhouyuan Huo Ben Hutchinson Justin Hsu Martin Jaggi Tara Javidi Gauri Joshi Mikhail Khodak Jakub Konecn\u00fd Aleksandra Korolova Farinaz Koushanfar Sanmi Koyejo Tancr\u00e8de Lepoint Yang Liu Prateek Mittal Mehryar Mohri Richard Nock Ayfer \u00d6zg\u00fcr Rasmus Pagh Hang Qi Daniel Ramage Ramesh Raskar Mariana Raykova Dawn Song Weikang Song Sebastian U. Stich Ziteng Sun Ananda Theertha Suresh Florian Tram\u00e8r Praneeth Vepakomma Jianyu Wang Li Xiong Zheng Xu Qiang Yang Felix X. Yu Han Yu and Sen Zhao. 2021. Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning 14 1-2 (2021) 1--210.","DOI":"10.1561\/9781680837896"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1378600.1378630"},{"key":"e_1_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Seungwoo Kang Youngki Lee Chulhong Min Younghyun Ju Taiwoo Park Jinwon Lee Yunseok Rhee and Junehwa Song. 2010. Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments. In 2010 ieee international conference on pervasive computing and communications (percom). IEEE 135--144.","DOI":"10.1109\/PERCOM.2010.5466982"},{"key":"e_1_2_2_31_1","volume-title":"Proc. Int. Conf. on Machine Learning (ICML).","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. 2020. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. In Proc. Int. Conf. on Machine Learning (ICML)."},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070942.2070968"},{"key":"e_1_2_2_33_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. In NeurIPS Workshop on Private Multi-Party Machine Learning.","author":"Kone\u010dn\u00fd Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u00fd, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. In NeurIPS Workshop on Private Multi-Party Machine Learning."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477114.3488760"},{"key":"e_1_2_2_35_1","volume-title":"Proc. USENIX Sym. on Operating Systems Design and Implementation (OSDI).","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient Federated Learning via Guided Participant Selection. In Proc. USENIX Sym. on Operating Systems Design and Implementation (OSDI)."},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2013.68"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450006"},{"key":"e_1_2_2_38_1","volume-title":"Proc. Int. Conf. on Machine Learning (ICML).","author":"Li Tian","year":"2021","unstructured":"Tian Li, Shengyuan Hu, Ahmand Beirami, and Virginia Smith. 2021. Ditto: Fair and Robust Federated Learning Through Personalization. In Proc. Int. Conf. on Machine Learning (ICML)."},{"key":"e_1_2_2_39_1","volume-title":"Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2019","unstructured":"Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2019. Federated Learning: Challenges, Methods, and Future Directions. arXiv abs\/1908.07873 (2019)."},{"key":"e_1_2_2_40_1","volume-title":"Proc. Conf. on Machine Learning and Systems (MLSys).","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. In Proc. Conf. on Machine Learning and Systems (MLSys)."},{"key":"e_1_2_2_41_1","volume-title":"Proc. Int. Conf. on Learning Representations (ICLR).","author":"Li Tian","year":"2020","unstructured":"Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2020. Fair Resource Allocation in Federated Learning. In Proc. Int. Conf. on Learning Representations (ICLR)."},{"key":"e_1_2_2_42_1","volume-title":"Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623","author":"Li Xiaoxiao","year":"2021","unstructured":"Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. 2021. Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)."},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994374.2994388"},{"key":"e_1_2_2_44_1","first-page":"1","article-title":"DistFL: Distribution-aware Federated Learning for Mobile Scenarios","volume":"5","author":"Liu Bingyan","year":"2021","unstructured":"Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, and Xiangqun Chen. 2021. DistFL: Distribution-aware Federated Learning for Mobile Scenarios. 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In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162). PMLR, 14461--14484. https:\/\/2.zoppoz.workers.dev:443\/https\/proceedings.mlr.press\/v162\/lubana22a.html"},{"key":"e_1_2_2_47_1","volume-title":"Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 (2020)."},{"key":"e_1_2_2_48_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. 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Conf. on Learning Representations (ICLR).","author":"Reddi Sashank J.","year":"2021","unstructured":"Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dn\u00fd, Sanjiv Kumar, and Hugh Brendan McMahan. 2021. Adaptive Federated Optimization. In Proc. Int. Conf. on Learning Representations (ICLR)."},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSRS.2017.8272822"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA52953.2021.00184"},{"key":"e_1_2_2_60_1","volume-title":"Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints. arXiv abs\/1910.01991","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints. arXiv abs\/1910.01991 (2019)."},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2020.3045135"},{"key":"e_1_2_2_62_1","volume-title":"Proc. Adv. in Neural Information Processing Systems (NeurIPS).","author":"Smith Virginia","year":"2017","unstructured":"Virginia Smith, Chao-Kai chiang, Maziar Sanjabi, and Ameet Talwalkar. 2017. Federated Multi-Task Learning. In Proc. Adv. in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_2_63_1","unstructured":"SpeedTest. 2021. Speedtest Global Index. https:\/\/2.zoppoz.workers.dev:443\/https\/www.speedtest.net\/global-index."},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"e_1_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2017.7917864"},{"key":"e_1_2_2_67_1","volume-title":"Position-aware activity recognition with wearable devices. Pervasive and mobile computing 38","author":"Sztyler Timo","year":"2017","unstructured":"Timo Sztyler, Heiner Stuckenschmidt, and Wolfgang Petrich. 2017. Position-aware activity recognition with wearable devices. Pervasive and mobile computing 38 (2017), 281--295."},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485946"},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161192"},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"e_1_2_2_71_1","volume-title":"A knowledge-light approach to personalised and open-ended human activity recognition. Knowledge-based systems 192","author":"Wijekoon Anjana","year":"2020","unstructured":"Anjana Wijekoon, Nirmalie Wiratunga, Sadiq Sani, and Kay Cooper. 2020. A knowledge-light approach to personalised and open-ended human activity recognition. 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