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Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
Trust but verify: accountability for network services
Aydan R. Yumerefendi and Jeffrey S. Chase · 2004
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Accountability: definition and relationship to verifiability
Ralf Küsters, Tomasz Truderung, and Andreas Vogt · 2010
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Datalog and logic databases
Sergio Greco and Cristian Molinaro · 2015
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Verifying computations without reexecuting them
Michael Walfish and Andrew J Blumberg · 2015
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The science of the blockchain
Roger Wattenhofer · 2016
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Architecture of the hyperledger blockchain fabric
Christian Cachin et al · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Hyperledger fabric: a distributed operating system for permissioned blockchains
Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, Konstantinos Christidis, Angelo De Caro, David Enyeart, Christopher Ferris, Gennady Laventman, Yacov Manevich, et al · 2018
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The global landscape of AI ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena · 2019
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Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector, June 2019
David Leslie · 2019
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The role and limits of principles in AI ethics: towards a focus on tensions
Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave · 2019
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Principles alone cannot guarantee ethical AI
Brent Mittelstadt · 2019
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Factsheets: Increasing trust in ai services through supplier’s declarations of conformity
Matthew Arnold, Rachel K.E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, and Kush R. Varshney · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes · 2020
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What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability
Maranke Wieringa · 2020
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The relationship between trust in ai and trustworthy machine learning technologies
Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad Van Moorsel · 2020
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Toward trustworthy AI development: mechanisms for supporting verifiable claims
Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, and Ruth Fong · 2020
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Hybridalpha: An efficient approach for privacy-preserving federated learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
Cited alongside, same era.
Automated verification of accountability in security protocols
Robert Künnemann, Ilkan Esiyok, and Michael Backes · 2019
Cited alongside, same era.
What does not fit can be made to fit! trade-offs in distributed ledger technology designs
Niclas Kannengießer, Sebastian Lins, Tobias Dehling, and Ali Sunyaev · 2019
Cited alongside, same era.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
Cited alongside, same era.
Adaptive histogram-based gradient boosted trees for federated learning, 2020
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo, and Heiko Ludwig · 2020
Cited alongside, same era.
Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 2020
Cited alongside, same era.
Mitigating bias in federated learning, 2020
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
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Robin Bloomfield and John Rushby · 2020
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Ibm federated learning: an enterprise framework white paper v0. 1, 2020
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al · 2020
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Building and auditing fair algorithms: A case study in candidate screening
Christo Wilson, Avijit Ghosh, Shan Jiang, Alan Mislove, Lewis Baker, Janelle Szary, Kelly Trindel, and Frida Polli · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al · 2021
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https://ai.googleblog.com/2017/04/federated-learning-collaborative.html , 2017
“Federated learning: Collaborative machine learning without centralized training data” · 2021
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https://www.fedai.org/cases/utilization-of-fate-in-risk-management-of-credit-in-small-and-micro-enterprises/ , 2019
“Utilization of fate in risk management of credit in small and micro enterprises” · 2021
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Designing accountable systems
Severin Kacianka and Alexander Pretschner · 2021
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Accountability in the decentralised-adversary setting
Robert Kunnemann, Deepak Garg, and Michael Backes · 2021
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