Fetching the paper…
Reading the bibliography…
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing.
Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
Earlier work this paper cites.
Carbon-Aware Load Balancing for Geo-distributed Cloud Services. In 21st Int. Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)
Zhi Zhou, Fangming Liu, Yong Xu, Ruolan Zou, Hong Xu, John C.S. Lui, and Hai Jin. 2013 · 2013
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2016 · 2016
Earlier work this paper cites.
Different Models for Forecasting Wind Power Generation: Case Study
David B. Alencar, Carolina de Mattos Affonso, Roberto C. L. Oliveira, Jorge Laureano Moya Rodríguez, Jandecy Cabral Leite, and Jose Carlos R. Filho. 2017 · 2017
Earlier work this paper cites.
Improved satellite-derived PV power nowcasting using real-time power data from reference PV systems
Jamie M. Bright, Sven Killinger, David Lingfors, and Nicholas A. Engerer. 2018 · 2017
Earlier work this paper cites.
Densely Connected Convolutional Networks. In CVPR
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017 · 2017
Earlier work this paper cites.
Managing Battery Aging for High Energy Availability in Green Datacenters
Longjun Liu, Hongbin Sun, Chao Li, Tao Li, Jingmin Xin, and Nanning Zheng. 2017 · 2017
Earlier work this paper cites.
Towards Federated Learning at Scale: System Design. In MLSys
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019 · 2019
Earlier work this paper cites.
LEAF: A Benchmark for Federated Settings. In Workshop on Federated Learning for Data Privacy and Confidentiality at NeurIPS
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2019 · 2019
Earlier work this paper cites.
Zero-carbon Cloud: Research Challenges for Datacenters as Supply-following Loads
Andrew A Chien, Chaojie Zhang, and Hai Duc Nguyen. 2019 · 2019
Earlier work this paper cites.
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Harry Hsu, Hang Qi, and Matthew Brown. 2019 · 2019
Earlier work this paper cites.
Prediction of Solar Power Generation Based on Random Forest Regressor Model. In IEEE SIBIRCON
Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, T. Chakravarthy, Venu Gopal Gasi, Sandeep Kumar Yadav Bollu, Raphael Caire, Sai Kumar Reddy Atluri, and Suresh Karrolla. 2019 · 2019
Earlier work this paper cites.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In ICML
Mingxing Tan and Quoc Le. 2019 · 2019
Earlier work this paper cites.
Federated learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019 · 2019
Earlier work this paper cites.
Flower: A Friendly Federated Learning Research Framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane. 2020 · 2020
Earlier work this paper cites.
The carbon impact of artificial intelligence
Payal Dhar. 2020 · 2020
Earlier work this paper cites.
Power Budgeting of Big Data Applications in Container-based Clusters. In IEEE CLUSTER
Jonatan Enes, Guillaume Fieni, Roberto R. Expósito, Romain Rouvoy, and Juan Touriño. 2020 · 2020
Earlier work this paper cites.
Federated Learning in Smart City Sensing: Challenges and Opportunities
Ji Chu Jiang, Burak Kantarci, Sema Oktug, and Tolga Soyata. 2020 · 2020
Earlier work this paper cites.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, and M. Jorge Cardoso. 2020 · 2020
Earlier work this paper cites.
Energy and Policy Considerations for Modern Deep Learning Research. In AAAI
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2020 · 2020
Earlier work this paper cites.
Borg: The next Generation. In EuroSys
Muhammad Tirmazi, Adam Barker, Nan Deng, Md E. Haque, Zhijing Gene Qin, Steven Hand, Mor Harchol-Balter, and John Wilkes. 2020 · 2020
Earlier work this paper cites.
Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers
Jiajia Zheng, Andrew A. Chien, and Sangwon Suh. 2020 · 2020
Earlier work this paper cites.
Keyword Transformer: A Self-Attention Model for Keyword Spotting. In Proc. Interspeech 2021 . 4249–4253
Axel Berg, Mark O’Connor, and Miguel Tairum Cruz. 2021 · 2021
Cited alongside, same era.
Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources
Gilbert Fridgen, Marc-Fabian Körner, Steffen Walters, and Martin Weibelzahl. 2021 · 2021
Cited alongside, same era.
A Framework for Sustainable Federated Learning. In 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt)
Başak Güler and Aylin Yener. 2021 · 2021
Cited alongside, same era.
Made to measure: Sustainability commitment progress and updates
Lucas Joppa. 2021 · 2021
Cited alongside, same era.
Oort: Efficient Federated Learning via Guided Participant Selection. In USENIX OSDI
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021 · 2021
Cited alongside, same era.
2022 Environmental Sustainability Report
Microsoft. 2022 · 2022
Later among the works it cites.
Deep Federated Learning for Autonomous Driving. In 2022 IEEE Intelligent Vehicles Symposium (IV)
Anh Nguyen, Tuong Do, Minh Tran, Binh X. Nguyen, Chien Duong, Tu Phan, Erman Tjiputra, and Quang D. Tran. 2022 · 2022
Later among the works it cites.
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and Jeff Dean. 2022 · 2022
Later among the works it cites.
Carbon-Aware Computing for Datacenters
Ana Radovanovic, Ross Koningstein, Ian Schneider, Bokan Chen, Alexandre Duarte, Binz Roy, Diyue Xiao, Maya Haridasan, Patrick Hung, Nick Care, Saurav Talukdar, Eric Mullen, Kendal Smith, Mariellen Cottman, and Walfredo Cirne. 2022 · 2022
Later among the works it cites.
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems. In AAAI
Martin Rapp, Ramin Khalili, Kilian Pfeiffer, and Jörg Henkel. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model
Qing’an Li, Chang Cai, Yasunari Kamada, Takao Maeda, Yuto Hiromori, Shuni Zhou, and Jianzhong Xu. 2021 · 2021
Cited alongside, same era.
Evaluating Coupling Models for Cloud Datacenters and Power Grids. In ACM e-Energy
Liuzixuan Lin, Victor M. Zavala, and Andrew Chien. 2021 · 2021
Cited alongside, same era.
Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning. In Workshop on Socially Responsible Machine Learning at ICML
Rakshit Naidu, Harshita Diddee, Ajinkya K Mulay, Aleti Vardhan, Krithika Ramesh, and Ahmed Zamzam. 2021 · 2021
Cited alongside, same era.
A first look into the carbon footprint of federated learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro Porto Buarque de Gusmao, Daniel J. Beutel, Taner Topal, Akhil Mathur, and Nicholas D. Lane. 2021 · 2021
Cited alongside, same era.
Let’s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. In ACM Middleware
Philipp Wiesner, Ilja Behnke, Dominik Scheinert, Kordian Gontarska, and Lauritz Thamsen. 2021 · 2021
Cited alongside, same era.
Energy Efficient Federated Learning Over Wireless Communication Networks
Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, and Mohammad Shikh-Bahaei. 2021 · 2021
Cited alongside, same era.
Amazon’s 2022 Sustainability Report
Amazon. 2022 · 2022
Cited alongside, same era.
Renewables 2022 Global Status Report
REN21. 2022 · 2022
Later among the works it cites.
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Jinhyun So, Kevin Hsieh, Behnaz Arzani, Shadi Noghabi, Salman Avestimehr, and Ranveer Chandra. 2022 · 2022
Later among the works it cites.
A Survey on Participant Selection for Federated Learning in Mobile Networks. In Workshop on Mobility in the Evolving Internet Architecture (MobiArch) at MobiCom
Behnaz Soltani, Venus Haghighi, Adnan Mahmood, Quan Z. Sheng, and Lina Yao. 2022 · 2022
Later among the works it cites.
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running. In ICDCS
Cong Wang, Bin Hu, and Hongyi Wu. 2022 · 2022
Later among the works it cites.
MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters. In USENIX NSDI
Qizhen Weng, Wencong Xiao, Yinghao Yu, Wei Wang, Cheng Wang, Jian He, Yong Li, Liping Zhang, Wei Lin, and Yu Ding. 2022 · 2022
Later among the works it cites.
Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. In International European Conference on Parallel and Distributed Computing (Euro-Par)
Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz Thamsen, and Odej Kao. 2022 · 2022
Later among the works it cites.
Sustainable AI: Environmental Implications, Challenges and Opportunities. In MLSys
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, and Kim M. Hazelwood. 2022 · 2022
Later among the works it cites.
A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. In AAAI
Sai Qian Zhang, Jieyu Lin, and Qi Zhang. 2022 · 2022
Later among the works it cites.
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. In ICLR
Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, and Tom Goldstein. 2022 · 2022
Later among the works it cites.
REFL: Resource-Efficient Federated Learning. In EuroSys . ACM
Ahmed M. Abdelmoniem, Atal Narayan Sahu, Marco Canini, and Suhaib A. Fahmy. 2023 · 2023
Closest in time.
How we count carbon emissions from electricity matters
Jake Oster. 2022 · 2023
Closest in time.
Energy vs Privacy: Estimating the Ecological Impact of Federated Learning. In ACM e-Energy
René Schwermer, Ruben Mayer, and Hans-Arno Jacobsen. 2023 · 2023
Closest in time.
Ecovisor: A Virtual Energy System for Carbon-Efficient Applications. In ASPLOS
Abel Souza, Noman Bashir, Jorge Murillo, Walid Hanafy, Qianlin Liang, David Irwin, and Prashant Shenoy. 2023 · 2023
Closest in time.
A timely new approach to certifying clean energy
Maud Texier. 2021 · 2023
Closest in time.
A Testbed for Carbon-Aware Applications and Systems
Philipp Wiesner, Ilja Behnke, and Odej Kao. 2023 · 2023
Closest in time.
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, and Ilya Mironov. 2023 · 2023
Closest in time.
Managing oversupply
California ISO. 2024 · 2024
Closest in time.