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We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications.
Think locally, act globally: Federated learning with local and global representations
Liang, P. P.; Liu, T.; Ziyin, L.; Allen, N. B.; Auerbach, R. P.; Brent, D.; Salakhutdinov, R.; and Morency, L.-P. 2020 · 2001
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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 · 2003
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When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Ma, C.; Li, J.; Ding, M.; Shi, L.; Wang, T.; Han, Z.; and Poor, H. V. 2020 · 2009
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Sensing as a service: Challenges, solutions and future directions
Sheng, X.; Tang, J.; Xiao, X.; and Xue, G. 2013 · 2013
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Lcars: a location-content-aware recommender system
Yin, H.; Sun, Y.; Cui, B.; Hu, Z.; and Chen, L. 2013 · 2013
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Big data analytics in healthcare: promise and potential
Raghupathi, W.; and Raghupathi, V. 2014 · 2014
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Applications of big data to smart cities
Al Nuaimi, E.; Al Neyadi, H.; Mohamed, N.; and Al-Jaroodi, J. 2015 · 2015
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The role of big data in smart city
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Data marketplace for Internet of Things
Mišura, K.; and Žagar, M. 2016 · 2016
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Practical secure aggregation for privacy-preserving machine learning
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y.; Su, L.; and Xu, J. 2017 · 2017
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Differentially private federated learning: A client level perspective
Geyer, R. C.; Klein, T.; and Nabi, M. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Blockchain Enabled Data Marketplace–Design and Challenges
Banerjee, P.; and Ruj, S. 2018 · 2018
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Systematic review of smartphone-based passive sensing for health and wellbeing
Cornet, V. P.; and Holden, R. J. 2018 · 2018
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I3: An iot marketplace for smart communities
Krishnamachari, B.; Power, J.; Kim, S. H.; and Shahabi, C. 2018 · 2018
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IDMoB: IoT data marketplace on blockchain
Özyilmaz, K. R.; Doğan, M.; and Yurdakul, A. 2018 · 2018
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Towards a decentralized data marketplace for smart cities
Ramachandran, G. S.; Radhakrishnan, R.; and Krishnamachari, B. 2018 · 2018
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Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations
Wang, Y.; Kung, L.; and Byrd, T. A. 2018 · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D.; Chen, Y.; Kannan, R.; and Bartlett, P. 2018 · 2018
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N.; Chakraborty, S.; Mittal, P.; and Calo, S. 2019 · 2019
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Aggregating crowd wisdom via blockchain: A private, correct, and robust realization
Duan, H.; Zheng, Y.; Du, Y.; Zhou, A.; Wang, C.; and Au, M. H. 2019 · 2019
Towards fair and privacy-preserving federated deep models
Lyu, L.; Yu, J.; Nandakumar, K.; Li, Y.; Ma, X.; Jin, J.; Yu, H.; and Ng, K. S. 2020 · 2020
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Biscotti: A Blockchain System for Private and Secure Federated Learning
Shayan, M.; Fung, C.; Yoon, C. J.; and Beschastnikh, I. 2020 · 2020
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Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Wang, H.; Sreenivasan, K.; Rajput, S.; Vishwakarma, H.; Agarwal, S.; Sohn, J.-y.; Lee, K.; and Papailiopoulos, D. 2020 · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Wei, K.; Li, J.; Ding, M.; Ma, C.; Yang, H. H.; Farokhi, F.; Jin, S.; Quek, T. Q.; and Poor, H. V. 2020 · 2020
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Privacy-preserving blockchain-based federated learning for IoT devices
Zhao, Y.; Zhao, J.; Jiang, L.; Tan, R.; Niyato, D.; Li, Z.; Lyu, L.; and Liu, Y. 2020 · 2020
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Blockchained on-device federated learning
Kim, H.; Park, J.; Bennis, M.; and Kim, S.-L. 2019 · 2019
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RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Li, L.; Xu, W.; Chen, T.; Giannakis, G. B.; and Ling, Q. 2019 · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z.; Song, M.; Zhang, Z.; Song, Y.; Wang, Q.; and Qi, H. 2019 · 2019
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Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach
Yang, H.; Zhang, X.; Fang, M.; and Liu, J. 2019 · 2019
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How to backdoor federated learning
Bagdasaryan, E.; Veit, A.; Hua, Y.; Estrin, D.; and Shmatikov, V. 2020 · 2020
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Secure single-server aggregation with (poly) logarithmic overhead
Bell, J. H.; Bonawitz, K. A.; Gascón, A.; Lepoint, T.; and Raykova, M. 2020 · 2020
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Zhu, L.; and Han, S. 2020 · 2020
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Unlocking the value of privacy: Trading aggregate statistics over private correlated data
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