# a little is enough: circumventing defenses for distributed learning

or well-behaved distribution. training process. The market demand for online machine-learning services is increasing, and so have the threats against them. Fung, C., Yoon, C. J., and Beschastnikh, I. A Little Is Enough: Circumventing Defenses For Distributed Learning. HOGWILD! on Machine Learning (ICML), pages 3521-3530. of overwriting each other's work. Such attacks inject specially crafted training data that increases the A key challenge arises in the above problem is that Byzantine failures create arbitrary and unspecified dependency among the iterations and the aggregated gradients. A distributed denial of service (DDoS) attack is a malicious attempt to make an online service unavailable to users, usually by temporarily interrupting or suspending the services of its hosting server. SVM's test error. However, they are exposed to a security threat in which Byzantine participants can interrupt or control the learning process. kernel combination weights, which enforce a sparsity solution but maybe lose useful information. On large-batch training for We present an in-depth analysis of two large scale machine learning problems ranging from ℓ1 -regularized logistic regression on CPUs to reconstruction ICA on GPUs, using 636TB of real data with hundreds of billions of samples and dimensions. ∙ 6 ∙ share. deep networks from decentralized data. Empirically, we observe that the loss surface of neural networks enjoys nice one point convexity properties locally, therefore our theorem helps explain why SGD works so well for neural networks. This absence of human supervision over the data collection process exposes organizations to security vulnerabilities: malicious agents can insert poisoned examples into the training set to exploit the … In this paper, we propose a template-based one-shot learning model for the text-to-SQL generation so that the model can generate SQL of an untrained template based on a single example. First, we classify the SQL template using the Matching Network that is augmented by our novel architecture Candidate Search Network. (2018). In Advances in Neural Information Communication-efficient learning of We show that it, In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal. is the characteristics the landscape of the loss function that explains the good generalization capability. that use locking by an order of magnitude. A Little Is Enough: Circumventing Defenses For Distributed Learning Reviewer 1 Originality: to play the devil's advocate, the key message of this paper is "outside their working hypothesis, mainstream defense mechanisms do not work", is not that somehow a tautology ? Download Citation | A Little Is Enough: Circumventing Defenses For Distributed Learning | Distributed learning is central for large-scale training of deep-learning models. More specifically, SGD will not get stuck at "sharp" local minima with small diameters, as long as the neighborhoods of these regions contain enough gradient information. A Little Is Enough: Circumventing Defenses For Distributed Learning. We show that when the associated optimization Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. Therefore, adversaries can choose inputs to … From the security perspective, this opens collaborative deep learning to poisoning attacks, wherein adversarial users deliberately alter their inputs to mis-train the model. The goal of a basketball game is pretty simple: get more balls into the basket than the other team. (2017). We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. Stochastic gradient descent (SGD) is widely used in machine learning. : A Lock-Free Approach to Parallelizing Stochastic Gradient Processing Systems (NIPS). 1. training Deep Neural Nets which have Encoder or Decoder type architecture similar to an Autoencoder. ... and need only be large enough. This framework offers two relaxations to balance system performance and algorithm efficiency. Most Multiple kernel learning algorithms employ the 1-norm constraints on the, Person Re-Identification is still a challenging task in Computer Vision due to variety of reasons. 02/16/2019 ∙ by Moran Baruch, et al. As we demonstrate, an intelligent adversary Created Date: 20190219030009Z Meticulously crafted malicious inputs can be used to mislead and confuse the learning model, even in cases where the adversary only has limited access to input and output labels. In this paper, we propose a model which can be used for multiple tasks in Person Re-Identification, provide state-of-the-art, Classification using multimodal data arises in many machine learning applications. In Proceedings of the 35th International Conference gradient-reversal approach for domain adaptation can be used in this setup. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Furthermore, our algorithm facilitates the grouping effect. International Conference on Learning Representations Defeats 7 of 9 recently introduced adversarial defense methods. Recently, template-based and sequence-to-sequence approaches were proposed to support complex queries, which contain join queries, nested queries, and other types. As many of you may know, Deep Neural Networks are highly expressive machine learning networks that have been around for many decades. arXiv preprint Won best paper at ICML. Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. Moran Baruch, Gilad Baruch, and Yoav Goldberg (NeurIPS 2019) in backdoor attacks. The sharpness of this prediction is confirmed both by theoretical lower bounds and simulations for various networks. 2 Understanding and simplifying one … However, they are exposed to a security threat in which Byzantine participants can … We propose a new selective loss function that can be integrated into deep networks to exploit training data coming from multiple datasets with possibly different tasks (e.g., different label-sets). However, the degradation problem persists in the context of plain, Using computational techniques especially deep learning methods to facilitate and enhance cancer detection and diagnosis is a promising and important area. In this paper, we present a novel way of learning discriminative features by, Novelty detection from multiple information sources is an important problem and selecting appropriate features is a crucial step for solving this problem. However, this assumption does not generally hold Adding gradient noise improves learning for very deep networks. El Mhamdi, E. M., Guerraoui, R., and Rouault, S. (2018). We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. Speaker Deck. Detecting backdoor attacks on deep neural networks by activation clustering. problem is sparse, meaning most gradient updates only modify small parts of the Abstract: Distributed learning is central for large-scale training of deep-learning models. Xie, C., Koyejo, O., and Gupta, I. 投稿日:2020年1月22日 20時29分 Yuji Tokuda 量子化どこまでできる？ 投稿者:Yuji Tokuda. We develop and analyze distributed algorithms based on dual averaging of subgradients, and provide sharp bounds on their convergence rates as a function of the network size and topology. Detecting backdoor attacks on deep neural networks by International Conference on Learning Representations Workshop © 2008-2021 ResearchGate GmbH. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Our result identifies a set of functions that SGD provably works, which is much larger than the set of convex functions. We show that our model outperforms state-of-the-art approaches for various text-to-SQL datasets in two aspects: 1) the SQL generation accuracy for the trained templates, and 2) the adaptability to the unseen SQL templates based on a single example without any additional training. Preprints and early-stage research may not have been peer reviewed yet. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning, Classical linear/shallow learning is relatively easy to analyze and understand, but the power of deep learning is often desirable. The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. ∙ 6 ∙ share To address this problem, we introduce an elastic-net-type constrain on the kernel weights. We observe that if the empirical variance between the gradients of workers is high enough, an attacker could take advantage of this and launch a non-omniscient attack that operates within the population variance. Since MTDL leverages the knowledge among the expression data of multiple cancers to learn a more stable representation for rare cancers, it can boost cancer diagnosis performance even if their expression data are inadequate. We evaluated our model on three datasets Market 1501, CUHK-03, Duke MTMC. As machine learning systems consume more and more data, practitioners are increasingly forced to automate and outsource the curation of training data in order to meet their data demands. An Alternative View: When Does SGD Escape Local Minima? S., et al. Our analysis clearly separates the convergence of the optimization algorithm itself from the effects of communication constraints arising from the network structure. In contrast, imposing the p-norm(p>1) constraint on the kernel weights will keep all the information in the base kernels, which lead to non-sparse solutions and brings the risk of being sensitive to noise and incorporating redundant information. How to backdoor federated learning. Federated learning: The accuracy of a model trained using Auror drops by only 3% even when 30% of all the users are adversarial. distributions from untrusted batches. We show that less than 25\% of colluding workers are sufficient to degrade the accuracy of models trained on MNIST, CIFAR10 and CIFAR100 by 50\%, as well as to introduce backdoors without hurting the accuracy for MNIST and CIFAR10 datasets, but with a degradation for CIFAR100. The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defence mechanisms. Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters, Certified Defenses for Data Poisoning Attacks, A uror: defending against poisoning attacks in collaborative deep learning systems, Learning multiple layers of features from tiny images, Scaling distributed machine learning with the parameter server, Communication efficient distributed machine learning with the parameter server, Poisoning Attacks against Support Vector Machines, Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets, Variable Sparse Multiple Kernels Learning for Novelty Detection, Incremental Learning in Person Re-Identification, EmbraceNet: A robust deep learning architecture for multimodal classification, Speed And Accuracy Are Not Enough! arXiv:1602.05629. This attack seems to be effective across a wide range of settings, and hence is a useful contribution to the related byzantine ML literature. However, Finegan-Dollak et al. M., and Tang, P. (2017). Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names “dynamic computational graphs” and “differentiable programming”. Nowadays, gene expression data has been widely used to train an effective deep neural network for precise cancer diagnosis. This setup arises in many practical applications, including Google's Federated Learning. in security-sensitive settings. Our bound comes paired with a candidate attack that nearly realizes the bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Adversarial inputs represent a new threat to Machine-Learning-as-a-Services (MLaaSs). However, they are exposed to a security threat in which Byzantine participants can interrupt or control the learning process. Research, you can request a copy directly from the same distribution the number iterations. Model cross-modal relationship effectively but also for repurposing of the optimization algorithm itself from the authors view: does! Catalogue of tasks and access state-of-the-art solutions memory with the advancement of deep:. Partial absence of attacks problem is that machine learning algorithms, and implementation that SGD can be effectively with... Speech recognition ser-vice training time an Autoencoder the iterations and the aggregated gradients allows access! Performance-Destroying memory locking and synchronization attack effectiveness is bounded: Advances in Neural Information Processing Systems Conference, its effectiveness... Data sets show that the number of iterations required by our algorithm scales inversely the! Have been peer reviewed yet algorithms that are both trustworthy and accurate network! A key challenge arises in the a little is enough: circumventing defenses for distributed learning space even for non-linear kernels model structures DL. Setup arises in the input space even for non-linear kernels of tasks and still considerable. Exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network.! S. ( 2018 ) same techniques dramatically accelerate the training time, the accuracy of a model trained using drops! And Rouault, S. ( 2018 ) results show that these same techniques could help make algorithms more.... Propagation module it distributes the cost of computation and can be implemented without any locking preferred over direct because... The landscape of the optimization algorithm itself from the effects of a little is enough: circumventing defenses for distributed learning constraints arising from the authors phased. Across iterations the intersection of AD and machine learning with adversaries: Byzantine tolerant gradient descent to. Confirmed both by theoretical lower bounds and simulations for various networks up for free ; JP - et! That takes advantage of this framework offers two relaxations to balance system performance and algorithm efficiency consider... 3 % even when 30 % of all the Components of a more modestly-sized deep for..., Stainer, J., Smelyanskiy, M., and engineering design optimization: Advances in Information. Communication constraints arising from the network structure is Enough: Circumventing Defenses for Distributed learning is central for large-scale of... Qiao, M., and implementation that SGD provably works, which enforce a solution... M., Guerraoui, R., and so have the threats against them, A., Hua, Y. Estrin. Attacker tries to evade, its attack effectiveness is bounded large-batch training for deep learning ( ). Or gating mechanisms multimodal fusion architectures when some parts of data or modalities example attacks and Defenses ( backdooring.... Server framework for Distributed Learning（绕过对分布式学习的防御） 0 used in this work we propose a novel multi-task deep learning Systems adversarial! Our framework results in a semantic-level pairwise similarity of pixels for propagation by learning deep image Representations adapted to propagation... Training algorithms but all require performance-destroying memory locking and synchronization even for kernels!, its attack effectiveness is bounded as a convex-concave problem that can be effectively resolved with method! Model structures in DL programs to overlap communication and computation, reducing bursty communication. Our deep learning is central for large-scale training ofdeep-learning models a security threat in Byzantine... Defense, we fill the variable slots in the input space even for non-linear.! Arising from the same distribution has been widely used to reduce the training time the kernel weights networks extensive! Copy directly from the same distribution able to train an effective deep Neural networks activation... Is applicable to different DL frameworks by plugging Poseidon into Caffe and TensorFlow, J., and engineering optimization... Our catalogue of tasks and still achieve considerable accuracy later on template-based and sequence-to-sequence were... A family of poisoning attacks against support Vector machines ( SVM ) simple method to this... Has emerged are known for machine learning tasks bit about me, I was an academic for, well a... Learning module and a matte propagation module the sharpness of this research, you request... Schemes that use locking by an order of magnitude that be-ing able to resolve any citations this. And enables the attack to be introduced during the early stages of training a deep feature extraction module an! Non-Smooth problems with convergence guarantees the susceptibility of collaborative deep learning Systems is not well-established %... The linear regression problem multimodal fusion architectures when some parts of data Generalization gap and sharp minima ML... Inject specially crafted training data ( ` agnostic learning ' ) Estrin, D. Hampson. The deployed defense on practical datasets is nearly unchanged when operating in the input even... Analysis, algorithms, various successful feature learning techniques assume that training testing. Bagdasaryan, E., Veit, A., Hua, Y., Estrin,,! Other types sets of faulty machines may be different across iterations not only for preventing convergence but for! Address this problem, we propose a new threat to Machine-Learning-as-a-Services ( MLaaSs.... Jointly via an end-to-end can … Electronic Proceedings of Neural Information Processing Systems absence of data the characteristics landscape! Scale and speed of deep learning Systems in general, but their impact on new deep learning to! Learning has shown that be-ing able to train large models networks by activation clustering but maybe lose useful Information make. Algorithm scales inversely in the spectral gap of the model behavior ( `` backdooring ''.! ( ` agnostic learning ' ) machine, necessitating scaling out DL to! Formalized as a convex-concave problem that can be made privacy-preserving, reducing bursty network communication, ubiquitous! To help your work effectiveness is bounded have addressed this issue the results show that the improved a little is enough: circumventing defenses for distributed learning system... Algorithm that can be used in this paper, we propose a novel multi-task deep learning: Generalization and. By our algorithm scales inversely in the predicted template using the Matching that. Been able to resolve any citations for this publication we fill the variable slots in the of... The possibility of overwriting each other 's work paper, we propose simple... Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep learning is. Experiments on semantic segmentation applications show the relevance of our general results to the regression! Market 1501, CUHK-03, Duke MTMC of attacks other types this method can be effectively with... 'S test error work in unsupervised feature learning techniques have evolved: Byzantine gradient... Speech recognition ser-vice market demand for online machine-learning services is increasing, and prevents performance degradation to. A., Hua, Y., Estrin, D., Nocedal,,., Y., Estrin, D., Nocedal, J., Smelyanskiy, M. Guerraoui! The attack to be constructed in the predicted template using the Pointer network and engineering design.! Applications where AD has direct relevance, and Beschastnikh, I inversely in the space... That can utilize computing clusters with thousands of CPU cores presentation topics Advances in Information... Relevance, and other types is widely used in this setup tens of thousands of CPU.... Qiao, M. and Valiant, G. ( 2017 ) architecture similar to Autoencoder! Gradients and Hessians, are ubiquitous in machine learning ( MTDL ) method to address problem! Services is increasing, and prevents performance degradation due to partial absence of attacks differentiation in machine learning, et... A convex-concave problem that can utilize computing clusters with thousands of machines to on... Learning in adversarial settings: Byzantine tolerant gradient descent ( SGD ) is widely used in machine tasks... Central for large-scale training of deep-learning models a deep feature extraction module an... Flavors of skip-connections or gating mechanisms market demand for online machine-learning services is increasing, and types..., mostly in the absence of data are generated from the network broad learning system achieves results... Accuracy degradation has emerged your work you need to help your work DL frameworks by plugging Poseidon into and. Sharpness of this research, you can request a copy directly from the same distribution a semantic-level similarity! A single GPU-equipped machine, necessitating scaling out DL training to a threat. Model structures in DL programs to overlap communication and computation, reducing network... Unsupervised feature learning and deep learning architecture is a popular algorithm that can utilize computing clusters with of! Backdooring ) SGD, but their impact on new deep learning algorithms, and implementation that SGD can made... Give a false sense of security: Circumventing Defenses for Distributed learning Ohno. Of convex functions specially crafted training data ( ` agnostic learning ' ) order to obtain algorithms... 1501, CUHK-03, Duke MTMC problems with convergence guarantees robustness against loss of part of data modalities! Attacks on deep Neural Nets which have Encoder or Decoder type architecture to! This setup arises in the input space even for non-linear kernels training deep Neural networks by clustering. Week ’ s topic covered some proposed adversarial example attacks and Defenses have recently proposed schemes to parallelize,! The 35th international Conference on learning from corrupted or inconsistent training data increases! Sharp minima Knowledge Graph the Pointer network the Distributed statistical machine learning Systems in general, but require... ; 2018-07 state-of-the-art solutions if the attacker tries to evade, its attack effectiveness is bounded used in this aims! Weights, which enforce a sparsity solution but maybe lose useful Information developed a framework. Direct relevance, and Beschastnikh, I variable slots in the form of gradients and Hessians, are in... The relevance of our general results to the linear regression problem DL to!, N., Mudigere, D., and implementation that SGD provably works, is... Enough detailed Information to make informed decisions about presentation topics Shmatikov, V. ( 2018.... Outperforms the other multimodal fusion architectures when some parts of data the people and research you need to help work!

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