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Surv."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today\u2019s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it is imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it is crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.<\/jats:p>","DOI":"10.1145\/3718363","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T06:29:26Z","timestamp":1739600966000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":68,"title":["Green Federated Learning: A New Era of Green Aware AI"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2895-1425","authenticated-orcid":false,"given":"Dipanwita","family":"Thakur","sequence":"first","affiliation":[{"name":"DIMES, University of Calabria, Arcavacata di Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3159-0536","authenticated-orcid":false,"given":"Antonella","family":"Guzzo","sequence":"additional","affiliation":[{"name":"University of Calabria, Arcavacata di Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-891X","authenticated-orcid":false,"given":"Giancarlo","family":"Fortino","sequence":"additional","affiliation":[{"name":"University of Calabria, Arcavacata di Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-2496","authenticated-orcid":false,"given":"Francesco","family":"Piccialli","sequence":"additional","affiliation":[{"name":"University of Naples Federico II, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"International Conference on Learning Representations","year":"2021","unstructured":"Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. 2021. 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Comparing biases for minimal network construction with back-propagation. In Advances in Neural Information Processing Systems, D. Touretzky (Ed.), Vol. 1. Morgan-Kaufmann."},{"issue":"7","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"9366","DOI":"10.1109\/TVT.2023.3246636","article-title":"Three-stage Stackelberg game enabled clustered federated learning in heterogeneous UAV swarms","volume":"72","author":"He Wenji","year":"2023","unstructured":"Wenji HeHaipeng Yao, Tianle Mai, Fu Wang, and Mohsen Guizani. 2023. Three-stage Stackelberg game enabled clustered federated learning in heterogeneous UAV swarms. 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FedTiny: Pruned federated learning towards specialized tiny models. Retrieved from https:\/\/openreview.net\/forum?id=2WmBMrCZSx"},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Jiyue Huang Chi Hong Yang Liu Lydia Y. Chen and Stefanie Roos. 2023. Maverick matters: Client contribution and selection in federated learning. In Advances in Knowledge Discovery and Data Mining Springer Nature Switzerland Cham 269\u2013282.","DOI":"10.1007\/978-3-031-33377-4_21"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"53854","DOI":"10.1109\/ACCESS.2022.3173288","article-title":"Federated learning-based explainable anomaly detection for industrial control systems","volume":"10","year":"2022","unstructured":"Truong Thu Huong, Ta Phuong Bac, Kieu Ngan Ha, Nguyen Viet Hoang, Nguyen Xuan Hoang, Nguyen Tai Hung, and Kim Phuc Tran. 2022. Federated learning-based explainable anomaly detection for industrial control systems. 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Now Foundations.","DOI":"10.1561\/9781680837896"},{"key":"e_1_3_1_58_2","first-page":"1","article-title":"Blockchain-based federated learning with SMPC model verification against poisoning attack for healthcare systems","author":"Kalapaaking Aditya Pribadi","year":"2023","unstructured":"Aditya Pribadi Kalapaaking, Ibrahim Khalil, and Xun Yi. 2023. Blockchain-based federated learning with SMPC model verification against poisoning attack for healthcare systems. IEEE Transactions on Emerging Topics in Computing (2023), 1\u201311.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"issue":"2","key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1109\/TVT.2021.3055767","article-title":"Optimizing task assignment for reliable blockchain-empowered federated edge learning","volume":"70","year":"2021","unstructured":"Jiawen Kang, Zehui Xiong, Xuandi Li, Yang Zhang, Dusit Niyato, Cyril Leung, and Chunyan Miao. 2021. Optimizing task assignment for reliable blockchain-empowered federated edge learning. IEEE Transactions on Vehicular Technology 70, 2 (2021), 1910\u20131923.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_60_2","series-title":"Proceedings of Machine Learning Research","first-page":"5132","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"III Hal Daum\u00e9","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 Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 119), Hal Daum\u00e9 III and Aarti Singh (Eds.). PMLR, 5132\u20135143."},{"key":"e_1_3_1_61_2","first-page":"8101","volume-title":"The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI\u201921)","author":"Khirirat Sarit","year":"2021","unstructured":"Sarit Khirirat, Sindri Magn\u00fasson, Arda Aytekin, and Mikael Johansson. 2021. A flexible framework for communication-efficient machine learning: From HPC to IoT. In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI\u201921). 8101\u20138109."},{"key":"e_1_3_1_62_2","series-title":"MICRO\u201921","first-page":"183","volume-title":"MICRO-54: 54th Annual IEEE\/ACM International Symposium on Microarchitecture","author":"Kim Young Geun","year":"2021","unstructured":"Young Geun Kim and Carole-Jean Wu. 2021. AutoFL: Enabling heterogeneity-aware energy efficient federated learning. In MICRO-54: 54th Annual IEEE\/ACM International Symposium on Microarchitecture (Virtual Event, Greece) (MICRO\u201921). Association for Computing Machinery, 183\u2013198."},{"key":"e_1_3_1_63_2","unstructured":"Alexandre Lacoste Alexandra Luccioni Victor Schmidt and Thomas Dandres. 2019. Quantifying the carbon emissions of machine learning. arxiv:1910.09700 [cs.CY]"},{"key":"e_1_3_1_64_2","volume-title":"USENIX Symposium on Operating Systems Design and Implementation","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. Efficient federated learning via guided participant selection. In USENIX Symposium on Operating Systems Design and Implementation."},{"key":"e_1_3_1_65_2","volume-title":"USENIX Symposium on Operating Systems Design and Implementation","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. Efficient federated learning via guided participant selection. In USENIX Symposium on Operating Systems Design and Implementation."},{"key":"e_1_3_1_66_2","first-page":"7615","volume-title":"IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Lai Yi-Cheng","year":"2024","unstructured":"Yi-Cheng Lai, Chen-Yu Wang, and Feng-Tsun Chien. 2024. When green learning meets federated learning: Toward distributed learning with low complexity and model heterogeneity. In IEEE International Conference on Acoustics, Speech and Signal Processing. 7615\u20137619."},{"key":"e_1_3_1_67_2","unstructured":"Namhoon Lee Thalaiyasingam Ajanthan and Philip H. S. Torr. 2019. SNIP: Single-shot network pruning based on connection sensitivity. arxiv:1810.02340 [cs.CV]"},{"key":"e_1_3_1_68_2","series-title":"MobiCom\u201922","first-page":"158","volume-title":"Proc. of the 28th Annual International Conference on Mobile Computing And Networking","author":"Li Chenning","year":"2022","unstructured":"Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao. 2022. PyramidFL: A fine-grained client selection framework for efficient federated learning. In Proc. of the 28th Annual International Conference on Mobile Computing And Networking (Sydney, NSW, Australia) (MobiCom\u201922). Association for Computing Machinery, 158\u2013171."},{"key":"e_1_3_1_69_2","volume-title":"International Conference on Learning Representations","author":"Li Hao","year":"2017","unstructured":"Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. 2017. Pruning filters for efficient convnets. In International Conference on Learning Representations."},{"key":"e_1_3_1_70_2","first-page":"1","volume-title":"IEEE GLOBECOM","author":"Li Peichun","year":"2021","unstructured":"Peichun Li, Xumin Huang, Miao Pan, and Rong Yu. 2021. FedGreen: Federated learning with fine-grained gradient compression for green mobile edge computing. In IEEE GLOBECOM. 1\u20136."},{"key":"e_1_3_1_71_2","volume-title":"Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li Qinbin","year":"2021","unstructured":"Qinbin Li, Bingsheng He, and Dawn Song. 2021. Model-contrastive federated learning. In Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_1_72_2","first-page":"1","volume-title":"IEEE International Conference on Communications (ICC\u201920)","year":"2020","unstructured":"Shiqi Li, Qi Qi, Jingyu Wang, Haifeng Sun, Yujian Li, and F. Richard Yu. 2020. GGS: General gradient sparsification for federated learning in edge computing. In IEEE International Conference on Communications (ICC\u201920). 1\u20137."},{"issue":"3","key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50\u201360.","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_3_1_74_2","first-page":"551","volume-title":"39th International Conference on Computer Design","author":"Lian Zirui","year":"2021","unstructured":"Zirui Lian, Jing Cao, Yanru Zuo, Weihong Liu, and Zongwei Zhu. 2021. AGQFL: Communication-efficient federated learning via automatic gradient quantization in edge heterogeneous systems. In 39th International Conference on Computer Design. 551\u2013558."},{"key":"e_1_3_1_75_2","volume-title":"NeurIPS 2019 Workshop on Federated Learning","year":"2019","unstructured":"Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B. Allen, Randy P. Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2019. Think locally, act globally: Federated learning with local and global representations. In NeurIPS 2019 Workshop on Federated Learning."},{"issue":"01","key":"e_1_3_1_76_2","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1109\/TNET.2023.3299851","article-title":"Accelerating federated learning with data and model parallelism in edge computing","volume":"32","author":"Liao Y.","year":"2024","unstructured":"Y. Liao, Y. Xu, H. Xu, Z. Yao, L. Wang, and C. Qiao. 2024. Accelerating federated learning with data and model parallelism in edge computing. IEEE\/ACM Transactions on Networking 32, 01 (2024), 904\u2013918.","journal-title":"IEEE\/ACM Transactions on Networking"},{"issue":"3","key":"e_1_3_1_77_2","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","year":"2020","unstructured":"Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao. 2020. Federated learning in mobile edge networks: A comprehensive survey. 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In IEEE Conference on Computer Vision and Pattern Recognition. 806\u2013814."},{"issue":"2","key":"e_1_3_1_80_2","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1109\/TNSM.2023.3280515","article-title":"Multilevel federated learning-based intelligent traffic flow forecasting for transportation network management","volume":"20","year":"2023","unstructured":"Lei Liu, Yuxing Tian, Chinmay Chakraborty, Jie Feng, Qingqi Pei, Li Zhen, and Keping Yu. 2023. Multilevel federated learning-based intelligent traffic flow forecasting for transportation network management. 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In ICCV."},{"key":"e_1_3_1_84_2","first-page":"1739","volume-title":"2022 IEEE INFOCOM","author":"Luo Bing","year":"2022","unstructured":"Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, and Leandros Tassiulas. 2022. Tackling system and statistical heterogeneity for federated learning with adaptive client sampling. In 2022 IEEE INFOCOM (London, United Kingdom). IEEE Press, 1739\u20131748."},{"issue":"1","key":"e_1_3_1_85_2","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JIOT.2022.3201310","article-title":"Communication-efficient federated multitask learning over wireless networks","volume":"10","author":"Ma Haoyu","year":"2023","unstructured":"Haoyu Ma, Huayan Guo, and Vincent K. N. Lau. 2023. Communication-efficient federated multitask learning over wireless networks. IEEE Internet of Things Journal 10, 1 (2023), 609\u2013624.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_86_2","volume-title":"Proceedings of the 39th ICML","volume":"162","author":"Makhija Disha","year":"2022","unstructured":"Disha Makhija, Xing Han, Nhat Ho, and Joydeep Ghosh. 2022. Architecture agnostic federated learning for neural networks. In Proceedings of the 39th ICML, Vol. 162. PMLR."},{"key":"e_1_3_1_87_2","unstructured":"H. B. McMahan Eider Moore D. Ramage S. Hampson and B. A. Y. Arcas. 2016. Communication-efficient learning of deep networks from decentralized data. (2016)."},{"key":"e_1_3_1_88_2","first-page":"349","volume-title":"IEEE\/ACM 7th Symposium on Edge Computing","author":"Mendula Matteo","year":"2022","unstructured":"Matteo Mendula and Paolo Bellavista. 2022. Energy-aware edge federated learning for enhanced reliability and sustainability. In IEEE\/ACM 7th Symposium on Edge Computing. 349\u2013354."},{"key":"e_1_3_1_89_2","unstructured":"Pavlo Molchanov Stephen Tyree Tero Karras Timo Aila and Jan Kautz. 2017. Pruning convolutional neural networks for resource efficient inference. arxiv:1611.06440 [cs.LG]"},{"key":"e_1_3_1_90_2","doi-asserted-by":"crossref","first-page":"44205","DOI":"10.1109\/ACCESS.2023.3273123","article-title":"Distillation and ordinary federated learning actor-critic algorithms in heterogeneous UAV-aided networks","volume":"11","author":"Nasr-Azadani Maedeh","year":"2023","unstructured":"Maedeh Nasr-Azadani, Jamshid Abouei, and Konstantinos N. Plataniotis. 2023. Distillation and ordinary federated learning actor-critic algorithms in heterogeneous UAV-aided networks. 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Surv. 55, 3, Article 60 (2022), 37 pages.","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/JSAC.2020.3036952","article-title":"Fast-convergent federated learning","volume":"39","year":"2021","unstructured":"Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, and H. Vincent Poor. 2021. Fast-convergent federated learning. IEEE Journal on Selected Areas in Communications 39, 1 (2021), 201\u2013218.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"e_1_3_1_94_2","first-page":"1","article-title":"Client selection for federated learning with heterogeneous resources in mobile edge","author":"Nishio Takayuki","year":"2018","unstructured":"Takayuki Nishio and Ryo Yonetani. 2018. Client selection for federated learning with heterogeneous resources in mobile edge. 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Association for Computing Machinery 54\u201366.","DOI":"10.1145\/3458864.3467681"},{"key":"e_1_3_1_97_2","unstructured":"Matthew J. Page Joanne E. McKenzie Patrick M. Bossuyt Isabelle Boutron Tammy C. Hoffmann Cynthia D. Mulrow Larissa Shamseer Jennifer M. Tetzlaff Elie A. Akl Sue E. Brennan and others. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372:n71 (2021)."},{"key":"e_1_3_1_98_2","first-page":"536","volume-title":"IEEE Wireless Communications and Networking Conference","author":"Park Jonghwan","year":"2022","unstructured":"Jonghwan Park, Dohyeok Kwon, and Songnam Hong. 2022. FedQOGD: Federated quantized online gradient descent with distributed time-series data. In IEEE Wireless Communications and Networking Conference. 536\u2013541."},{"key":"e_1_3_1_99_2","doi-asserted-by":"crossref","first-page":"74579","DOI":"10.1109\/ACCESS.2023.3296143","article-title":"Implementing privacy-preserving and collaborative industrial artificial intelligence","volume":"11","author":"Peres Ricardo Silva","year":"2023","unstructured":"Ricardo Silva Peres, Alexandre Manta-Costa, and Jos\u00e9 Barata. 2023. Implementing privacy-preserving and collaborative industrial artificial intelligence. IEEE Access 11 (2023), 74579\u201374589.","journal-title":"IEEE Access"},{"issue":"14","key":"e_1_3_1_100_2","first-page":"334","article-title":"Federated learning for computationally constrained heterogeneous devices: A survey","volume":"55","author":"Pfeiffer Kilian","year":"2023","unstructured":"Kilian Pfeiffer, Martin Rapp, Ramin Khalili, and J\u00f6rg Henkel. 2023. Federated learning for computationally constrained heterogeneous devices: A survey. ACM Comput. Surv. 55, 14s, Article 334 (2023), 27 pages.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_1_101_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/OJCS.2022.3206407","article-title":"Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge","volume":"3","year":"2022","unstructured":"Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir. 2022. Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge. 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Can Federated Learning Save The Planet?"},{"key":"e_1_3_1_105_2","first-page":"1","article-title":"A first look into the carbon footprint of federated learning","volume":"24","year":"2023","unstructured":"Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro Porto Buarque de Gusmao, Yan Gao, Daniel J. Beutel, Taner Topal, Akhil Mathur, and Nicholas D. Lane. 2023. A first look into the carbon footprint of federated learning. Journal of Machine Learning Research 24 (2023), 1\u201323.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_106_2","first-page":"281","volume-title":"IEEE GLOBECOM","author":"Qu Linping","year":"2022","unstructured":"Linping Qu, Shenghui Song, and Chi-Ying Tsui. 2022. FedDQ: Communication-efficient federated learning with descending quantization. In IEEE GLOBECOM. 281\u2013286."},{"key":"e_1_3_1_107_2","doi-asserted-by":"crossref","first-page":"124682","DOI":"10.1109\/ACCESS.2021.3111118","article-title":"Challenges, applications and design aspects of federated learning: A survey","volume":"9","year":"2021","unstructured":"K. M. Jawadur Rahman, Faisal Ahmed, Nazma Akhter, Mohammad Hasan, Ruhul Amin, Kazi Ehsan Aziz, A. K. M. Muzahidul Islam, Md. Saddam Hossain Mukta, and A. K. M. Najmul Islam. 2021. Challenges, applications and design aspects of federated learning: A survey. IEEE Access 9 (2021), 124682\u2013124700.","journal-title":"IEEE Access"},{"issue":"2","key":"e_1_3_1_108_2","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/JBHI.2022.3171852","article-title":"Federated learning for privacy preservation of healthcare data from smartphone-based side-channel attacks","volume":"27","author":"Rehman Abdul","year":"2023","unstructured":"Abdul Rehman, Imran Razzak, and Guandong Xu. 2023. Federated learning for privacy preservation of healthcare data from smartphone-based side-channel attacks. IEEE Journal of Biomedical and Health Informatics 27, 2 (2023), 684\u2013690.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_1_109_2","volume-title":"International Conference on Artificial Intelligence and Statistics","year":"2019","unstructured":"Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani. 2019. FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization. In International Conference on Artificial Intelligence and Statistics."},{"issue":"1","key":"e_1_3_1_110_2","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TGCN.2023.3309657","article-title":"Convergence analysis and latency minimization for semi-federated learning in massive IoT networks","volume":"8","author":"Ren Jianyang","year":"2024","unstructured":"Jianyang Ren, Wanli Ni, Hui Tian, and Gaofeng Nie. 2024. Convergence analysis and latency minimization for semi-federated learning in massive IoT networks. IEEE Transactions on Green Communications and Networking 8, 1 (2024), 413\u2013426.","journal-title":"IEEE Transactions on Green Communications and Networking"},{"issue":"1","key":"e_1_3_1_111_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JSTSP.2022.3224590","article-title":"Federated learning under intermittent client availability and time-varying communication constraints","volume":"17","author":"Ribero M\u00f3nica","year":"2023","unstructured":"M\u00f3nica Ribero, Haris Vikalo, and Gustavo de Veciana. 2023. Federated learning under intermittent client availability and time-varying communication constraints. IEEE Journal of Selected Topics in Signal Processing 17, 1 (2023), 98\u2013111.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"e_1_3_1_112_2","article-title":"\\(CO_2\\)  and greenhouse gas emissions","author":"Ritchie Hannah","year":"2023","unstructured":"Hannah Ritchie, Pablo Rosado, and Max Roser. 2023. \\(CO_2\\) and greenhouse gas emissions. Our World in Data (2023). https:\/\/ourworldindata.org\/co2-and-greenhouse-gas-emissions","journal-title":"Our World in Data"},{"key":"e_1_3_1_113_2","first-page":"3095","volume-title":"IEEE ICASSP","author":"Rizk Elsa","year":"2021","unstructured":"Elsa Rizk, Stefan Vlaski, and Ali H. Sayed. 2021. Optimal importance sampling for federated learning. In IEEE ICASSP. 3095\u20133099."},{"key":"e_1_3_1_114_2","unstructured":"Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar and Virginia Smith. 2020. Federated optimization in heterogeneous networks. In Proceedings of the Third Conference on Machine Learning and Systems MLSys 2020 Austin TX USA March 2-4 2020 Inderjit S. Dhillon Dimitris S. Papailiopoulos and Vivienne Sze (Eds.). mlsys.org."},{"key":"e_1_3_1_115_2","doi-asserted-by":"crossref","first-page":"16353","DOI":"10.1109\/ACCESS.2023.3244099","article-title":"Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G","volume":"11","year":"2023","unstructured":"Adeb Salh, Razali Ngah, Lukman Audah, Kwang Soon Kim, Qazwan Abdullah, Yahya M. Al-Moliki, Khaled A. Aljaloud, and Hairul Nizam Talib. 2023. Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G. 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Overcoming Forgetting in Federated Learning on Non-IID Data."},{"key":"e_1_3_1_123_2","series-title":"MobiArch \u201922","first-page":"19","volume-title":"Proc. of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture","author":"Soltani Behnaz","year":"2022","unstructured":"Behnaz Soltani, Venus Haghighi, Adnan Mahmood, Quan Z. Sheng, and Lina Yao. 2022. A survey on participant selection for federated learning in mobile networks. In Proc. of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture (Sydney, NSW, Australia) (MobiArch \u201922). Association for Computing Machinery, 19\u201324."},{"issue":"09","key":"e_1_3_1_124_2","doi-asserted-by":"crossref","first-page":"13693","DOI":"10.1609\/aaai.v34i09.7123","article-title":"Energy and policy considerations for modern deep learning research","volume":"34","author":"Strubell Emma","year":"2020","unstructured":"Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2020. Energy and policy considerations for modern deep learning research. Proc. of the AAAI Conference on Artificial Intelligence 34, 09 (Apr.2020), 13693\u201313696.","journal-title":"Proc. of the AAAI Conference on Artificial Intelligence"},{"issue":"12","key":"e_1_3_1_125_2","doi-asserted-by":"crossref","first-page":"4282","DOI":"10.1109\/TPDS.2022.3187365","article-title":"Eiffel: Efficient and fair scheduling in adaptive federated learning","volume":"33","author":"Sultana Abeda","year":"2022","unstructured":"Abeda Sultana, Md. Mainul Haque, Li Chen, Fei Xu, and Xu Yuan. 2022. Eiffel: Efficient and fair scheduling in adaptive federated learning. 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FedSelect: Customized selection of parameters for fine-tuning during personalized federated learning. arxiv:2306.13264 [cs.LG]"},{"key":"e_1_3_1_128_2","volume-title":"USENIX Workshop on Hot Topics in Edge Computing","author":"Tao Zeyi","year":"2018","unstructured":"Zeyi Tao and Qun Li. 2018. eSGD: Communication efficient distributed deep learning on the edge. In USENIX Workshop on Hot Topics in Edge Computing. USENIX Association, Boston, MA."},{"key":"e_1_3_1_129_2","first-page":"0636","volume-title":"IEEE Intl. Conf. on Dependable, Autonomic and Secure Computing, Intl. Conf. on Pervasive Intelligence and Computing (PiCom\u201923)","author":"Thakur Dipanwita","year":"2023","unstructured":"Dipanwita Thakur, Antonella Guzzo, and Giancarlo Fortino. 2023. Energy aware federated learning with application of activity recognition. In IEEE Intl. Conf. on Dependable, Autonomic and Secure Computing, Intl. 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Neural network quantization in federated learning at the edge. Information Sciences 575 (2021), 417\u2013436.","journal-title":"Information Sciences"},{"issue":"2","key":"e_1_3_1_132_2","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/COMST.2021.3058573","article-title":"Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems","volume":"23","author":"Wahab Omar Abdel","year":"2021","unstructured":"Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb. 2021. Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Communications Surveys & Tutorials 23, 2 (2021), 1342\u20131397.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"e_1_3_1_133_2","first-page":"498","volume-title":"2022 IEEE ICDM","author":"Wang B.","year":"2022","unstructured":"B. Wang, A. Li, M. Pang, H. 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Curran Associates Inc., Article 638, 13 pages."},{"issue":"6","key":"e_1_3_1_136_2","doi-asserted-by":"crossref","first-page":"4088","DOI":"10.1109\/TII.2021.3088057","article-title":"Federated transfer learning based cross-domain prediction for smart manufacturing","volume":"18","year":"2022","unstructured":"Kevin I-Kai Wang, Xiaokang Zhou, Wei Liang, Zheng Yan, and Jinhua She. 2022. Federated transfer learning based cross-domain prediction for smart manufacturing. IEEE Transactions on Industrial Informatics 18, 6 (2022), 4088\u20134096.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"2","key":"e_1_3_1_137_2","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/JBHI.2022.3157725","article-title":"Privacy-preserving federated learning for internet of medical things under edge computing","volume":"27","year":"2023","unstructured":"Ruijin Wang, Jinshan Lai, Zhiyang Zhang, Xiong Li, Pandi Vijayakumar, and Marimuthu Karuppiah. 2023. 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In Workshop of Federated Learning and Analytics in Practice, Colocated with 40th ICML, Honolulu, Hawaii, USA."},{"issue":"6","key":"e_1_3_1_153_2","doi-asserted-by":"crossref","first-page":"3318","DOI":"10.1109\/TMC.2021.3136853","article-title":"FedHAR: Semi-supervised online learning for personalized federated human activity recognition","volume":"22","year":"2023","unstructured":"Hongzheng Yu, Zekai Chen, Xiao Zhang, Xu Chen, Fuzhen Zhuang, Hui Xiong, and Xiuzhen Cheng. 2023. FedHAR: Semi-supervised online learning for personalized federated human activity recognition. IEEE Transactions on Mobile Computing 22, 6 (2023), 3318\u20133332.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_154_2","doi-asserted-by":"crossref","unstructured":"Xiaoming Yuan Jiahui Chen Jiayu Yang Ning Zhang Tingting Yang Tao Han and Amir Taherkordi. 2023. FedSTN: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction. 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