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Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge.
Differentially Private Learning with Adaptive Clipping
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Fair Resource Allocation in Federated Learning
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Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
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Federated Learning with Differential Privacy: Algorithms and Performance Analysis
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Adaptive Federated Optimization
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Using GPUs for machine learning algorithms. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05) . IEEE
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Flower: A Friendly Federated Learning Research Framework
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FedML: A Research Library and Benchmark for Federated Machine Learning
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The Algorithmic Foundations of Differential Privacy
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Quality-of-service in cloud computing: modeling techniques and their applications
Danilo Ardagna, Giuliano Casale, Michele Ciavotta, Juan F Pérez, and Weikun Wang. 2014 · 2014
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Unsupervised adaptive event detection for building-level energy disaggregation
Karim Said Barsim, Roman Streubel, and Bin Yang. 2014 · 2014
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Energy Consumption of Content Distribution from Nano Data Centers versus Centralized Data Centers
Fatemeh Jalali, Rob Ayre, Arun Vishwanath, Kerry Hinton, Tansu Alpcan, and Rod Tucker. 2014 · 2014
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Benchmarking Internet of things devices. In 2014 12th IEEE International Conference on Industrial Informatics (INDIN) . IEEE
C. P. Kruger and G. P. Hancke. 2014 · 2014
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Energy Consumption Comparison of Interactive Cloud-Based and Local Applications
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The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing. In 2016 23rd International Conference on Telecommunications (ICT) . IEEE
Yaser Jararweh, Ahmad Doulat, Omar AlQudah, Ejaz Ahmed, Mahmoud Al-Ayyoub, and Elhadj Benkhelifa. 2016 · 2016
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Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2016 · 2016
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Challenges and Opportunities in Edge Computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) . IEEE
Blesson Varghese, Nan Wang, Sakil Barbhuiya, Peter Kilpatrick, and Dimitrios S. Nikolopoulos. 2016 · 2016
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Privacy-Aware Offloading in Mobile-Edge Computing. In GLOBECOM 2017 - 2017 IEEE Global Communications Conference . IEEE
Xiaofan He, Juan Liu, Richeng Jin, and Huaiyu Dai. 2017 · 2017
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From Cloud Computing to Fog Computing: Unleash the Power of Edge and End Devices. In 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) . IEEE
Hua-Jun Hong. 2017 · 2017
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017 · 2017
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MEDAL. In Proceedings of the Eighth International Conference on Future Energy Systems . ACM
Thomas Kriechbaumer, Anwar Ul Haq, Matthias Kahl, and Hans-Arno Jacobsen. 2017 · 2017
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A Survey on Mobile Edge Computing: The Communication Perspective
Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B. Letaief. 2017 · 2017
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Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017 · 2017
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LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
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On Large-Cohort Training for Federated Learning. In Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (Eds.). Virtual Event
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith. 2021 · 2021
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SelfWatts: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters. In 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) . IEEE, Virtual Event
Guillaume Fieni, Romain Rouvoy, and Lionel Seiturier. 2021 · 2021
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FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay S. Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021 · 2021
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Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 14606–14619
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Chao Li, Yushu Xue, Jing Wang, Weigong Zhang, and Tao Li. 2018b · 2018
Cited alongside, same era.
Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
Maurizio Capra, Riccardo Peloso, Guido Masera, Massimo Ruo Roch, and Maurizio Martina. 2019 · 2019
Cited alongside, same era.
Deep Learning With Edge Computing: A Review
Jiasi Chen and Xukan Ran. 2019 · 2019
Cited alongside, same era.
A model for distributed in-network and near-edge computing with heterogeneous hardware
Ryan A. Cooke and Suhaib A. Fahmy. 2020 · 2019
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Pytorch lightning
William Falcon et al · 2019
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SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization . Association for Computational Linguistics, Hong Kong, China, 70–79
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
DeFog. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing . ACM
Jonathan McChesney, Nan Wang, Ashish Tanwer, Eyal de Lara, and Blesson Varghese. 2019 · 2019
Cited alongside, same era.
Aritra Mitra, Rayana Jaafar, George J. Pappas, and Hamed Hassani. 2021 · 2021
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Federated Reconstruction: Partially Local Federated Learning. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 11220–11232
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, and Sushant Prakash. 2021 · 2021
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A survey on deploying mobile deep learning applications: A systemic and technical perspective
Yingchun Wang, Jingyi Wang, Weizhan Zhang, Yufeng Zhan, Song Guo, Qinghua Zheng, and Xuanyu Wang. 2022d · 2021
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pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Daoyuan Chen, Dawei Gao, Weirui Kuang, Yaliang Li, and Bolin Ding. 2022 · 2022
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Robust Federated Learning With Noisy and Heterogeneous Clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 10072–10081
Xiuwen Fang and Mang Ye. 2022 · 2022
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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Mohammad Goudarzi, Marimuthu Palaniswami, and Rajkumar Buyya. 2022 · 2022
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Federated office plug-load identification for building management systems. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (Virtual Event). ACM, New York, NY, USA
René Schwermer, Jonas Buchberger, Ruben Mayer, and Hans-Arno Jacobsen. 2022 · 2022
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A Survey on Edge Performance Benchmarking
Blesson Varghese, Nan Wang, David Bermbach, Cheol-Ho Hong, Eyal De Lara, Weisong Shi, and Christopher Stewart. 2022 · 2022
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Communication-efficient federated learning via knowledge distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, and Xing Xie. 2022 · 2022
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FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, and Jingren Zhou. 2022 · 2022
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FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie, Xuchen Pan, Zitao Li, Yaliang Li, Bolin Ding, and Jingren Zhou. 2023 · 2023
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Edge Networking | Dell USA
Dell. 2023 · 2023
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Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
Radosvet Desislavov, Fernando Martínez-Plumed, and José Hernández-Orallo. 2023 · 2023
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Industrial Edge Devices
Siemens. 2023 · 2023
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FLINT: A Platform for Federated Learning Integration
Ewen Wang, Boyi Chen, Mosharaf Chowdhury, Ajay Kannan, and Franco Liang. 2023 · 2023
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Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
Xue-Yong Fu, Md Tahmid Rahman Laskar, Elena Khasanova, Cheng Chen, and Shashi Bhushan TN. 2024 · 2024
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A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. In The Twelfth International Conference on Learning Representations . ICLR, Vienna, Austria
Shiqiang Wang and Mingyue Ji. 2024 · 2024
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