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The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way.
NewsWeeder: Learning to Filter Netnews
Lang, K · 1995
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Early stopping-but when?
Prechelt, L · 2002
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Adaptive Federated Optimization, 2020
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2003
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Flower: A Friendly Federated Learning Research Framework, 2020
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusmão, P. P. B., and Lane, N. D · 2007
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FedML: A Research Library and Benchmark for Federated Machine Learning, 2020
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., Zhu, X., Wang, J., Shen, L., Zhao, P., Kang, Y., Liu, Y., Raskar, R., Yang, Q., Annavaram, M., and Avestimehr, S · 2007
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The law of recitals in European Community legislation
Klimas, T. and Vaiciukaite, J · 2008
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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The Algorithmic Foundations of Differential Privacy
Dwork, C. and Roth, A · 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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Deep Learning with Limited Numerical Precision
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P · 2015
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Trusted Execution Environment: What It is, and What It is Not
Sabt, M., Achemlal, M., and Bouabdallah, A · 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. A., and Tucker, R. S · 2015
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A Survey of Man In The Middle Attacks
Conti, M., Dragoni, N., and Lesyk, V · 2016
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Human behaviour as an aspect of cybersecurity assurance
Evans, M., Maglaras, L. A., He, Y., and Janicke, H · 2016
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Leveraging NVLINK and asynchronous data transfer to scale beyond the memory capacity of GPUs
Appelhans, D. and Walkup, B · 2017
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Learning under Concept Drift: A Review
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G · 2018
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Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
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Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect
Li, A., Song, S. L., Chen, J., Li, J., Liu, X., Tallent, N. R., and Barker, K. J · 2019
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al · 2020
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Inverting Gradients - How easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
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Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
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Privacy-preserving traffic flow prediction: A federated learning approach
Liu, Y., James, J., Kang, J., Niyato, D., and Zhang, S · 2020
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Energy Mix
Ritchie, H. and Rosado, P · 2020
Cited alongside, same era.
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
Xu, J. and Wang, H · 2020
Cited alongside, same era.
Differentially private learning with adaptive clipping
Andrew, G., Thakkar, O., McMahan, B., and Ramaswamy, S · 2021
Cited alongside, same era.
Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL - LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS, apr 2021
Council of the European Union · 2021
Cited alongside, same era.
Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
Cited alongside, same era.
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Greenhouse gas emission intensity of electricity generation, Oct 2023
European Environment Agency · 2023
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Electricity prices for household consumers, Oct 2023
Eurostat · 2023
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24/7 Carbon-Free Energy by 2030, 2023
Google · 2023
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FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs, 2023
Han, S., Buyukates, B., Hu, Z., Jin, H., Jin, W., Sun, L., Wang, X., Wu, W., Xie, C., Yao, Y., Zhang, K., Zhang, Q., Zhang, Y., Avestimehr, S., and He, C · 2023
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Rethinking Federated Learning With Domain Shift: A Prototype View
Huang, W., Ye, M., Shi, Z., Li, H., and Du, B · 2023
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Huang, Y., Gupta, S., Song, Z., Li, K., and Arora, S · 2021
Cited alongside, same era.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Li, X., JIANG, M., Zhang, X., Kamp, M., and Dou, Q · 2021
Cited alongside, same era.
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., and Srivastava, G · 2021
Cited alongside, same era.
Federated learning in a medical context: a systematic literature review
Pfitzner, B., Steckhan, N., and Arnrich, B · 2021
Cited alongside, same era.
Federated learning based energy demand prediction with clustered aggregation
Tun, Y. L., Thar, K., Thwal, C. M., and Hong, C. S · 2021
Cited alongside, same era.
Electricity consumer characteristics identification: A federated learning approach
Wang, Y., Bennani, I. L., Liu, X., Sun, M., and Zhou, Y · 2021
Cited alongside, same era.
A survey on federated learning
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y · 2021
Cited alongside, same era.
Jin, W., Yao, Y., Han, S., Joe-Wong, C., Ravi, S., Avestimehr, S., and He, C · 2023
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Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
Kim, M., Saad, W., Mozaffari, M., and Debbah, M · 2023
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Towards Unbounded Machine Unlearning
Kurmanji, M., Triantafillou, P., and Triantafillou, E · 2023
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A comprehensive survey to dataset distillation
Lei, S. and Tao, D · 2023
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Cefl: Carbon-efficient federated learning, 2023
Mehboob, T., Bashir, N., Iglesias, J. O., Zink, M., and Irwin, D · 2023
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A first look into the carbon footprint of federated learning
Qiu, X., Parcollet, T., Fernandez-Marques, J., Gusmao, P. P. B., Gao, Y., Beutel, D. J., Topal, T., Mathur, A., and Lane, N. D · 2023
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G
Salh, A., Ngah, R., Audah, L., Kim, K. S., Abdullah, Q., Al-Moliki, Y. M., Aljaloud, K. A., and Talib, H. N · 2023
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Cookie Pledge, 2023
The European Commission · 2023
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Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, 10 2023
The White House · 2023
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Data collection and quality challenges in deep learning: A data-centric ai perspective
Whang, S. E., Roh, Y., Song, H., and Lee, J.-G · 2023
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Fedzero: Leveraging renewable excess energy in federated learning
Wiesner, P., Khalili, R., Grinwald, D., Agrawal, P., Thamsen, L., and Kao, O · 2023
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FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Woisetschläger, H., Isenko, A., et al · 2023
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Machine Unlearning: A Survey
Xu, H., Zhu, T., Zhang, L., Zhou, W., and Yu, P. S · 2023
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Green Federated Learning, 2023
Yousefpour, A., Guo, S., Shenoy, A., Ghosh, S., Stock, P., Maeng, K., Krüger, S.-W., Rabbat, M., Wu, C.-J., and Mironov, I · 2023
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Efficient Membership Inference Attacks against Federated Learning via Bias Differences
Zhang, L., Li, L., Li, X., Cai, B., Gao, Y., Dou, R., and Chen, L · 2023
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FedPrompt: Communication-Efficient and Privacy-Preserving Prompt Tuning in Federated Learning
Zhao, H., Du, W., Li, F., Li, P., and Liu, G · 2023
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GDPR Enforcement Tracker, 01 2024
CMS Law · 2024
Closest in time.
Laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts, 01 2024
European Parliament and Council · 2024
Closest in time.
There are holes in Europe’s AI Act — and researchers can help to fill them
Nature · 2024
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