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The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets.
Green cloud computing: Balancing energy in processing, storage, and transport
Baliga, J., Ayre, R. W., Hinton, K., and Tucker, R. S · 2011
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Carat: Collaborative energy diagnosis for mobile devices
Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., and Tarkoma, S · 2013
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Modeling energy consumption of data transmission over Wi-Fi
Xiao, Y., Cui, Y., Savolainen, P., Siekkinen, M., Wang, A., Yang, L., Ylä-Jääski, A., and Tarkoma, S · 2013
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Energy consumption of content distribution from nano data centers versus centralized data centers
Jalali, F., Ayre, R., Vishwanath, A., Hinton, K., Alpcan, T., and Tucker, R · 2014
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Greendroid: A tool for analysing power consumption in the android ecosystem
Couto, M., Cunha, J., Fernandes, J. P., Pereira, R., and Saraiva, J · 2015
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Smartphone batteries: Better but no breakthrough
Network, D · 2015
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Energy consumption comparison of interactive cloud-based and local applications
Vishwanath, A., Jalali, F., Hinton, K., Alpcan, T., Ayre, R. W., and Tucker, R. S · 2015
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Practical secure aggregation for federated learning on user-held data
Bonawitz, K. A., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2016
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Mobile cpu’s rise to power: Quantifying the impact of generational mobile cpu design trends on performance, energy, and user satisfaction
Halpern, M., Zhu, Y., and Reddi, V. J · 2016
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Character-aware neural language models
Kim, Y., Jernite, Y., Sontag, D., and Rush, A. M · 2016
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Federated learning: Strategies for improving communication efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtarik, P., Suresh, A. T., and Bacon, D · 2016
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LEAF: A benchmark for federated settings
Caldas, S., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
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Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Applied federated learning: Improving Google keyboard query suggestions
Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., and Beaufays, F · 2018
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Towards federated learning at scale: System design
Bonawitz, K. A., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C. M., Konečný, J., Mazzocchi, S., McMahan, B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 2019
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On empirical comparisons of optimizers for deep learning
Choi, D., Shallue, C. J., Nado, Z., Lee, J., Maddison, C. J., and Dahl, G. E · 2019
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How Apple personalizes Siri without hoovering up your data
Hao, K · 2019
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Model pruning enables efficient federated learning on edge devices
Jiang, Y., Wang, S., Ko, B. J., Lee, W., and Tassiulas, L · 2019
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Quantifying the carbon emissions of machine learning
Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T · 2019
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PowerSGD: Practical low-rank gradient compression for distributed optimization
Vogels, T., Karimireddy, S. P., and Jaggi, M · 2019
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Machine learning at Facebook: Understanding inference at the edge
Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., et al · 2019
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Autoscale: Energy efficiency optimization for stochastic edge inference using reinforcement learning
Kim, Y. G. and Wu, C.-J · 2020
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The carbon footprint of foods: are differences explained by the impacts of methane?, March 2020
A first look into the carbon footprint of federated learning
Qiu, X., Parcollet, T., Fernandez-Marques, J., de Gusmao, P. P. B., Gao, Y., Beutel, D. J., Topal, T., Mathur, A., and Lane, N. D · 2021
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Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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Treehouse: A case for carbon-aware datacenter software
Anderson, T., Belay, A., Chowdhury, M., Cidon, A., and Zhang, I · 2022
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Batterylab: a collaborative platform for power monitoring
Bustamante, F. and Livshits, B · 2022
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Measuring the carbon intensity of AI in cloud instances
Dodge, J., Prewitt, T., des Combes, R. T., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., and Buchanan, W · 2022
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Papaya: Practical, private, and scalable federated learning
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Ritchie, H · 2020
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CO 2 and greenhouse gas emissions
Ritchie, H., Roser, M., and Rosado, P · 2020
Cited alongside, same era.
FetchSGD: Communication-efficient federated learning with sketching
Rothchild, D., Panda, A., Ullah, E., Ivkin, N., Stoica, I., Braverman, V., Gonzalez, J., and Arora, R · 2020
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Green AI
Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O · 2020
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Energy and policy considerations for modern deep learning research
Strubell, E., Ganesh, A., and McCallum, A · 2020
Cited alongside, same era.
Smart at what cost? characterising mobile deep neural networks in the wild
Almeida, M., Laskaridis, S., Mehrotra, A., Dudziak, L., Leontiadis, I., and Lane, N. D · 2021
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On large-cohort training for federated learning
Charles, Z., Garrett, Z., Huo, Z., Shmulyian, S., and Smith, V · 2021
Cited alongside, same era.
Huba, D., Nguyen, J., Malik, K., Zhu, R., Rabbat, M., Yousefpour, A., Wu, C.-J., Zhan, H., Ustinov, P., Srinivas, H., et al · 2022
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FedGPO: Heterogeneity-aware global parameter optimization for efficient federated learning
Kim, Y. G. and Wu, C.-J · 2022
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Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., and Huba, D · 2022
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The carbon footprint of machine learning training will plateau, then shrink
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., and Dean, J · 2022
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Reconciling security and communication efficiency in federated learning
Prasad, K., Ghosh, S., Cormode, G., Mironov, I., Yousefpour, A., and Stock, P · 2022
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al · 2022
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Compute trends across three eras of machine learning
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., and Villalobos, P · 2022
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Sustainable AI: Environmental implications, challenges and opportunities
Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Aga, F., Huang, J., Bai, C., et al · 2022
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Refl: Resource efficient federated learning
Abdelmoniem, A. M., Sahu, A. N., Canini, M., and Fahmy, S. A · 2023
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Meta data centers sustainability, b
Meta · 2023
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FLINT: A platform for federated learning integration
Wang, E., Kannan, A., Liang, Y., Chen, B., and Chowdhury, M · 2023
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