Research graph
References from Advances and Open Problems in Federated Learning. Local targets link to admitted publications; unresolved targets remain external evidence.
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On data banks and privacy homomorphisms
1978 · External reference
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Privacy-preserving SVM classification
10.1007/s10115-007-0073-7 · 2008 · External reference
Federated learning: Challenges, methods, and future directions
10.1109/msp.2020.2975749 · 2020 · External reference
Unresolved reference
2017 · External reference
Learning with privacy at scale
2017 · External reference
10.1145/2660267.2660348
10.1145/2660267.2660348 · External reference
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Federated learning for mobile keyboard prediction
2018 · External reference
Google’s Sundar Pichai: Privacy should not be a luxury good
2019 · External reference
Federated learning for emoji prediction in a mobile keyboard
2019 · External reference
Applied federated learning: Improving Google keyboard query suggestions
2018 · External reference
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Federated learning for keyword spotting
2018 · External reference
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Deep learning-based classification of mesothelioma improves prediction of patient outcome
2019 · External reference
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A generic framework for privacy preserving deep learning
2018 · External reference
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2015 · External reference
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Randomized gossip algorithms
10.1109/tit.2006.874516 · 2006 · External reference
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GADMM: Fast and communication efficient framework for distributed machine learning
2019 · External reference
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2019 · External reference
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Central server free federated learning over single-sided trust social networks
2019 · External reference
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Distributed learning over unreliable networks
2018 · External reference
Decentralized gradient methods: Does topology matter
2020 · External reference
10.1109/icc47138.2019.9123209
10.1109/icc47138.2019.9123209 · External reference
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2019 · External reference
Robust and communication-efficient collaborative learning
2019 · External reference
DeepSqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
2019 · External reference
10.1145/2684464.2684480
10.1145/2684464.2684480 · External reference
Privacy amplification by decentralization
2020 · External reference
Ethereum: A secure decentralised generalised transaction ledger
2014 · External reference
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Federated machine learning: Concept and applications
2019 · External reference
Se-cureBoost: A lossless federated learning framework
2019 · External reference
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
2017 · External reference
A communication efficient vertical federated learning framework
2019 · External reference
A secure federated transfer learning framework
10.1109/mis.2020.2988525 · 2020 · External reference
Learning privately over distributed features: An ADMM sharing approach
2019 · External reference
Backdoor attacks and defenses in feature-partitioned collaborative learning
2020 · External reference
A survey on transfer learning
10.1109/tkde.2009.191 · 2010 · External reference
IBM federated learning: An enterprise framework white paper V0.1
2020 · External reference
Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory
10.1109/jiot.2019.2940820 · 2019 · External reference
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A sustainable incentive scheme for federated learning
10.1109/mis.2020.2987774 · 2020 · External reference
Towards fair and privacy-preserving federated deep models
10.1109/tpds.2020.2996273 · 2020 · External reference
10.1145/3097983.3098118
10.1145/3097983.3098118 · External reference
10.1145/3357384.3357878
10.1145/3357384.3357878 · External reference
Distributed learning of deep neural network over multiple agents
2018 · External reference
Split learning for health: Distributed deep learning without sharing raw patient data
2018 · External reference
SplitNN-driven vertical partitioning
2018 · External reference
Detailed comparison of communication efficiency of split learning and federated learning
2019 · External reference
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ExpertMatcher: Automating ML model selection for clients using hidden representations
2019 · External reference
Nopeek: Information leakage reduction to share activations in distributed deep learning
10.1109/icdmw51313.2020.00134 · 2020 · External reference
Measuring and testing dependence by correlation of distances
10.1214/009053607000000505 · 2007 · External reference
Supervised dimensionality reduction via distance correlation maximization
10.1214/18-ejs1403 · 2018 · External reference
DISCO: Dynamic and invariant sensitive channel obfuscation for deep neural networks
2020 · External reference
A unifying view on dataset shift in classification
10.1016/j.patcog.2011.06.019 · 2012 · External reference
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2009 · External reference
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Dataset distillation
2018 · External reference
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2011 · External reference
Optimal distributed online prediction using mini-batches
2012 · External reference
An optimal method for stochastic composite optimization
10.1007/s10107-010-0434-y · 2012 · External reference
Parallel restarted SGD for non-convex optimization with faster convergence and less communication
2018 · External reference
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The error-feedback framework: Better rates for SGD with delayed gradients and compressed communication
2019 · External reference
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Communication trade-offs for synchronized distributed SGD with large step size
2019 · External reference
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
2019 · External reference
Is local SGD better than minibatch SGD?
2020 · External reference
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Local SGD with periodic averaging: Tighter analysis and adaptive synchronization
2019 · External reference
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10.1109/jsait.2021.3103770
10.1109/jsait.2021.3103770 · External reference
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Communication efficient decentralized training with multiple local updates
2019 · External reference
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On the convergence of Fed Avg on non-IID data
2019 · External reference
On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
2019 · External reference
Local adaalter: Communication-efficient stochastic gradient descent with adaptive learning rates
2019 · External reference
Adaptive subgradient methods for online learning and stochastic optimization
2011 · External reference
Adaptive bound optimization for online convex optimization
2010 · External reference
Adaptive federated optimization
2020 · External reference
Measuring the effects of non-identical data distribution for federated visual classification
2019 · External reference
SlowMo: Improving communication-efficient distributed SGD with slow momentum
2019 · External reference
Mime: Mimicking centralized stochastic algorithms in federated learning
2020 · External reference
DJAM: Distributed Jacobi asynchronous method for learning personal models
10.1109/lsp.2018.2859596 · 2018 · External reference
Federated evaluation of on-device personalization
2019 · External reference
A survey on multi-task learning
2017 · External reference
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Three approaches for personalization with applications to federated learning
2020 · External reference
A theory of learning from different domains
10.1007/s10994-009-5152-4 · 2010 · External reference
Domain adaptation and sample bias correction theory and algorithm for regression
10.1016/j.tcs.2013.09.027 · 2014 · External reference
Multiple-source adaptation with domain classifiers
2020 · External reference
Domain adaptation: Learning bounds and algorithms
2009 · External reference
A theory of multiple-source adaptation with limited target labeled data
2020 · External reference
A model of inductive bias learning
10.1613/jair.731 · 2000 · External reference
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On first-order meta-learning algorithms
2018 · External reference
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Improving federated learning personalization via model agnostic meta learning
2019 · External reference
Personalization of end-to-end speech recognition on mobile devices for named entities
10.1109/asru46091.2019.9003775 · 2019 · External reference
Personalized federated learning: A meta-learning approach
2020 · External reference
Differentially private meta-learning
2019 · External reference
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10.1007/978-3-642-31164-2
10.1007/978-3-642-31164-2 · 2012 · External reference
10.2139/ssrn.3549247
10.2139/ssrn.3549247 · External reference
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Salvaging federated learning by local adaptation
2020 · External reference
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StatLog: Comparison of classification algorithms on large real-world problems
10.1080/08839519508945477 · 1995 · External reference
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10.1007/978-1-4899-3099-6_2
10.1007/978-1-4899-3099-6_2 · 1993 · External reference
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BOHB: Robust and efficient hyperparameter optimization at scale
2018 · External reference
Hyperparameter optimization with approximate gradient
2016 · External reference
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On the outsized importance of learning rates in local update methods
2020 · External reference
10.1109/ieeeconf44664.2019.9049066
10.1109/ieeeconf44664.2019.9049066 · External reference
Differentially private learning with adaptive clipping
2019 · External reference
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Efficient multi-objective neural architecture search via Lamarckian evolution
2018 · External reference
DARTS: Differentiable architecture search
2018 · External reference
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SNAS: Stochastic neural architecture search
2018 · External reference
Weight agnostic neural networks
2019 · External reference
FedNAS: Federated deep learning via neural architecture search
2020 · External reference
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Federated learning: Strategies for improving communication efficiency
2016 · External reference
Inference under information constraints I: Lower bounds from chi-square contraction
10.1109/tit.2020.3028440 · 2020 · External reference
Lower bounds for learning distributions under communication constraints via Fisher information
2020 · External reference
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rTop-k: A statistical estimation approach to distributed SGD
10.1109/jsait.2020.3042094 · 2020 · External reference
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Qsparse-local-SGD: Distributed SGD with quantization, sparsification, and local computations
10.1109/jsait.2020.2985917 · 2020 · External reference
Natural compression for distributed deep learning
2019 · External reference
Randomized distributed mean estimation: Accuracy vs. communication
10.3389/fams.2018.00062 · 2018 · External reference
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Distributed fixed point methods with compressed iterates
2019 · External reference
Expanding the reach of federated learning by reducing client resource requirements
2018 · External reference
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Deep gradient compression: Reducing the communication bandwidth for distributed training
2017 · External reference
Robust and communication-efficient federated learning from non-IID data
2019 · External reference
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What is the state of neural network pruning?
2020 · External reference
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
2015 · External reference
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Model compression by entropy penalized reparameterization
2019 · External reference
To prune, or not to prune: Exploring the efficacy of pruning for model compression
2017 · External reference
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2012 · External reference
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VqSGD: Vector quantized stochastic gradient descent
2019 · External reference
Practical secure aggregation for federated learning on user-held data
2016 · External reference
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Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
2018 · External reference
Wireless network intelligence at the edge
2018 · External reference
Federated learning for ultra-reliable low-latency V2V communications
2018 · External reference
Over-the-air function computation in sensor networks
2016 · External reference
Harnessing wireless channels for scalable and privacy-preserving federated learning
10.1109/tcomm.2021.3078783 · External reference
Decentralized online learning: Take benefits from others’ data without sharing your own to track global trend
2019 · External reference
A comprehensive guide to Bayesian convolutional neural network with variational inference
2019 · External reference
Decentralized Bayesian learning over graphs
2019 · External reference
Privacy in deep learning: A survey
2020 · External reference
10.1145/3132747.3132769
10.1145/3132747.3132769 · External reference
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Private intersection-sum protocol with applications to attributing aggregate ad conversions
2017 · External reference
On deploying secure computing commercially: Private intersection-sum protocols and their business applications
2019 · External reference
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Privacy-preserving distributed linear regression on high-dimensional data
10.1515/popets-2017-0053 · 2017 · External reference
10.1145/3319535.3339819
10.1145/3319535.3339819 · External reference
10.1145/28395.28420
10.1145/28395.28420 · External reference
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Secure evaluation of quantized neural networks
2019 · External reference
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Somewhat practical fully homomorphic encryption
2012 · External reference
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2020 · External reference
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2020 · External reference
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2020 · External reference
A review of homomorphic encryption libraries for secure computation
2018 · External reference
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Turning HATE into LOVE: Homomorphic ad hoc threshold encryption for scalable MPC
2018 · External reference
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Architecture instruction set extensions programming reference
2012 · External reference
Intel SGX explained
2016 · External reference
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
2020 · External reference
A comprehensive comparison of multiparty secure additions with differential privacy
10.1109/tdsc.2015.2484326 · 2017 · External reference
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SEPIA: Privacy-preserving aggregation of multi-domain network events and statistics
2010 · External reference
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Untraceable electronic mail, return addresses, and digital pseudonyms
10.1145/358549.358563 · 1981 · External reference
Riffle
10.1515/popets-2016-0008 · 2016 · External reference
10.21236/ada465464
10.21236/ada465464 · 2004 · External reference
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Private information retrieval
10.1145/293347.293350 · 1998 · External reference
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A lattice-based computationally-efficient private information retrieval protocol
2007 · External reference
XPIR: Private information retrieval for everyone
10.1515/popets-2016-0010 · 2016 · External reference
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Communication-computation trade-offs in PIR
2019 · External reference
10.1109/allerton.2017.8262860
10.1109/allerton.2017.8262860 · External reference
10.1145/3243734.3243821
10.1145/3243734.3243821 · External reference
Private information retrieval with sublinear online time
2019 · External reference
10.1109/ccc.2005.2
10.1109/ccc.2005.2 · External reference
10.1109/isit.2018.8437805
10.1109/isit.2018.8437805 · External reference
10.1109/itw.2018.8613532
10.1109/itw.2018.8613532 · External reference
Private join and compute from PIR with default
2020 · External reference
10.1109/isit.2019.8849461
10.1109/isit.2019.8849461 · External reference
10.1109/itw44776.2019.8989319
10.1109/itw44776.2019.8989319 · External reference
10.1007/11681878_14
10.1007/11681878_14 · External reference
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The algorithmic foundations of differential privacy
10.1561/0400000042 · 2014 · External reference
Randomized response: A survey technique for eliminating evasive answer bias
10.1080/01621459.1965.10480775 · 1965 · External reference
What can we learn privately?
10.1137/090756090 · 2011 · External reference
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Differentially-private ‘draw and discard’ machine learning
2018 · External reference
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Distributed differentially private averaging with improved utility and robustness to malicious parties
2020 · External reference
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10.1007/978-3-030-26951-7\_22
10.1007/978-3-030-26951-7\_22 · External reference
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Scalable and differentially private distributed aggregation in the shuffled model
2019 · External reference
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On the power of multiple anonymous messages
2019 · External reference
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Computationally sound proofs
10.1137/s0097539795284959 · 2000 · External reference
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The knowledge complexity of interactive proof systems
10.1137/0218012 · 1989 · External reference
Pinocchio: Nearly practical verifiable computation
10.1145/2856449 · 2016 · External reference
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10.1145/2090236.2090263
10.1145/2090236.2090263 · External reference
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10.1145/3133956.3134104
10.1145/3133956.3134104 · External reference
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10.1109/sp.2018.00060
10.1109/sp.2018.00060 · External reference
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The secret sharer: Measuring unintended neural network memorization and extracting secrets
2018 · External reference
Extracting training data from large language models
2020 · External reference
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Exploiting unintended feature leakage in collaborative learning
2018 · External reference
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Theoretical guarantees for model auditing with finite adversaries
2019 · External reference
Protection against reconstruction and its applications in private federated learning
2018 · External reference
A general approach to adding differential privacy to iterative training procedures
2018 · External reference
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The composition theorem for differential privacy
10.1109/tit.2017.2685505 · 2017 · External reference
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Rényi differential privacy of the sampled Gaussian mechanism
2019 · External reference
Subsampled Rényi differential privacy and analytical moments accountant
2018 · External reference
Training production language models without memorizing user data
2020 · External reference
Tempered sigmoid activations for deep learning with differential privacy
2020 · External reference
Differentially private learning needs better features (or much more data)
2020 · External reference
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AdaCliP: Adaptive clipping for private SGD
2019 · External reference
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2021 · External reference
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Minimax rates of estimating approximate differential privacy
2019 · External reference
HEAX: High-performance architecture for computation on homomorphically encrypted data in the cloud
2019 · External reference
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The structure of optimal private tests for simple hypotheses
10.1145/3313276.3316336 · 2019 · External reference
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Practical locally private heavy hitters
2020 · External reference
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Optimal schemes for discrete distribution estimation under locally differential privacy
10.1109/tit.2018.2809790 · 2018 · External reference
Privacy loss in Apple’s implementation of differential privacy on MacOS 10.12
2017 · External reference
The power of the hybrid model for mean estimation
10.2478/popets-2020-0062 · 2020 · External reference
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1997 · External reference
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Shuffled model of federated learning: Privacy, communication and accuracy trade-offs
2020 · External reference
The distributed discrete gaussian mechanism for federated learning with secure aggregation
2021 · External reference
On the upload versus download cost for secure and private matrix multiplication
10.1109/itw44776.2019.8989342 · 2019 · External reference
On the capacity of secure distributed matrix multiplication
2019 · External reference
Secure federated submodel learning
2019 · External reference
Privacy for the protected (only)
2015 · External reference
Pufferfish: A framework for mathematical privacy definitions
10.1145/2514689 · 2014 · External reference
An economic analysis of privacy protection and statistical accuracy as social choices
10.1257/aer.20170627 · 2019 · External reference
Bringing differential privacy into the experimental economics lab: Theory and an application to a public-good game
2019 · External reference
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10.14722/ndss.2018.23291
10.14722/ndss.2018.23291 · External reference
How to backdoor federated learning
2018 · External reference
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Intriguing properties of neural networks
2013 · External reference
Targeted backdoor attacks on deep learning systems using data poisoning
2017 · External reference
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
2017 · External reference
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Explaining and harnessing adversarial examples
2015 · External reference
Towards deep learning models resistant to adversarial attacks
2017 · External reference
The Byzantine generals problem
10.1145/357172.357176 · 1982 · External reference
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10.1145/2933057.2933105
10.1145/2933057.2933105 · External reference
10.1109/jsac.2020.3041404
10.1109/jsac.2020.3041404 · External reference
Robust aggregation for federated learning
2019 · External reference
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Local model poisoning attacks to Byzantine-robust federated learning
2019 · External reference
A little is enough: Circumventing defenses for distributed learning
2019 · External reference
DETOX: A redundancy-based framework for faster and more robust gradient aggregation
2019 · External reference
Data encoding for byzantine-resilient distributed optimization
2020 · External reference
Backdoor attacks on federated meta-learning
2020 · External reference
SentiNet: Detecting physical attacks against deep learning systems
2018 · External reference
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Mitigating sybils in federated learning poisoning
2018 · External reference
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Zeno++: Robust asynchronous SGD with arbitrary number of Byzantine workers
2019 · External reference
BadNets: Identifying vulnerabilities in the machine learning model supply chain
2017 · External reference
Stronger data poisoning attacks break data sanitization defenses
2018 · External reference
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10.1609/aaai.v29i1.9569
10.1609/aaai.v29i1.9569 · External reference
A rotation and a translation suffice: Fooling CNNs with simple transformations
2017 · External reference
Testing robustness against unforeseen adversaries
2019 · External reference
Wasserstein adversarial examples via projected sinkhorn iterations
2019 · External reference
Adversarial machine learning at scale
2016 · External reference
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
2017 · External reference
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
2018 · External reference
Adversarial training for free
2019 · External reference
Feature denoising for improving adversarial robustness
10.1109/cvpr.2019.00059 · 2019 · External reference
Attacking the Madry defense model with L_1-based adversarial examples
2017 · External reference
Adversarial training and robustness for multiple perturbations
2019 · External reference
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Detecting backdoor attacks on deep neural networks by activation clustering
2018 · External reference
Can you really backdoor federated learning?
2019 · External reference
Attack of the tails: Yes, you really can backdoor federated learning
2020 · External reference
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Differentially private federated learning: A client level perspective
2017 · External reference
10.1109/sp.2019.00044
10.1109/sp.2019.00044 · External reference
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2019 · External reference
Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
2018 · External reference
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Fair resource allocation in federated learning
2019 · External reference
Layers of bias: A unified approach for understanding problems with risk assessment
10.1177/0093854818811379 · 2019 · External reference
Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice
2019 · External reference
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Censored and fair universal representations using generative adversarial models
2020 · External reference
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10.1145/3314183.3323847
10.1145/3314183.3323847 · External reference
Differentially private fair learning
2018 · External reference
Differential privacy has disparate impact on model accuracy
2019 · External reference
Fair decision making using privacy-protected data
2019 · External reference
10.1145/3319535.3354211
10.1145/3319535.3354211 · External reference
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Learning anonymized representations with adversarial neural networks
2018 · External reference
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Federated learning of predictive models from federated electronic health records
10.1016/j.ijmedinf.2018.01.007 · 2018 · External reference
Distributed deep learning networks among institutions for medical imaging
2018 · External reference
Current clinical use of polygenic scores will risk exacerbating health disparities
2019 · External reference
Device heterogeneity in federated learning: A superquantile approach
2020 · External reference
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Beyond individual and group fairness
2020 · External reference
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2020 · External reference
Post-stratification: A modeler’s perspective
10.1080/01621459.1993.10476368 · 1993 · External reference
Flower: A friendly federated learning research framework
2020 · External reference
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10.1145/2939672.2945397
10.1145/2939672.2945397 · External reference
Federated heavy hitters discovery with differential privacy
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