137,867 research outputs found
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning
Communication efficiency and privacy protection are two critical issues in
distributed machine learning. Existing methods tackle these two issues
separately and may have a high implementation complexity that constrains their
application in a resource-limited environment. We propose a comprehensive
quantization-based solution that could simultaneously achieve communication
efficiency and privacy protection, providing new insights into the correlated
nature of communication and privacy. Specifically, we demonstrate the
effectiveness of our proposed solutions in the distributed stochastic gradient
descent (SGD) framework by adding binomial noise to the uniformly quantized
gradients to reach the desired differential privacy level but with a minor
sacrifice in communication efficiency. We theoretically capture the new
trade-offs between communication, privacy, and learning performance
Blind quantum machine learning with quantum bipartite correlator
Distributed quantum computing is a promising computational paradigm for
performing computations that are beyond the reach of individual quantum
devices. Privacy in distributed quantum computing is critical for maintaining
confidentiality and protecting the data in the presence of untrusted computing
nodes. In this work, we introduce novel blind quantum machine learning
protocols based on the quantum bipartite correlator algorithm. Our protocols
have reduced communication overhead while preserving the privacy of data from
untrusted parties. We introduce robust algorithm-specific privacy-preserving
mechanisms with low computational overhead that do not require complex
cryptographic techniques. We then validate the effectiveness of the proposed
protocols through complexity and privacy analysis. Our findings pave the way
for advancements in distributed quantum computing, opening up new possibilities
for privacy-aware machine learning applications in the era of quantum
technologies.Comment: 11 pages, 3 figure
Adaptive Federated Minimax Optimization with Lower complexities
Federated learning is a popular distributed and privacy-preserving machine
learning paradigm. Meanwhile, minimax optimization, as an effective
hierarchical optimization, is widely applied in machine learning. Recently,
some federated optimization methods have been proposed to solve the distributed
minimax problems. However, these federated minimax methods still suffer from
high gradient and communication complexities. Meanwhile, few algorithm focuses
on using adaptive learning rate to accelerate algorithms. To fill this gap, in
the paper, we study a class of nonconvex minimax optimization, and propose an
efficient adaptive federated minimax optimization algorithm (i.e., AdaFGDA) to
solve these distributed minimax problems. Specifically, our AdaFGDA builds on
the momentum-based variance reduced and local-SGD techniques, and it can
flexibly incorporate various adaptive learning rates by using the unified
adaptive matrix. Theoretically, we provide a solid convergence analysis
framework for our AdaFGDA algorithm under non-i.i.d. setting. Moreover, we
prove our algorithms obtain lower gradient (i.e., stochastic first-order
oracle, SFO) complexity of with lower communication
complexity of in finding -stationary point
of the nonconvex minimax problems. Experimentally, we conduct some experiments
on the deep AUC maximization and robust neural network training tasks to verify
efficiency of our algorithms.Comment: Submitted to AISTATS-202
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
LOCKS: User Differentially Private and Federated Optimal Client Sampling
With changes in privacy laws, there is often a hard requirement for client
data to remain on the device rather than being sent to the server. Therefore,
most processing happens on the device, and only an altered element is sent to
the server. Such mechanisms are developed by leveraging differential privacy
and federated learning. Differential privacy adds noise to the client outputs
and thus deteriorates the quality of each iteration. This distributed setting
adds a layer of complexity and additional communication and performance
overhead. These costs are additive per round, so we need to reduce the number
of iterations. In this work, we provide an analytical framework for studying
the convergence guarantees of gradient-based distributed algorithms. We show
that our private algorithm minimizes the expected gradient variance by
approximately rounds, where d is the dimensionality of the model. We
discuss and suggest novel ways to improve the convergence rate to minimize the
overhead using Importance Sampling (IS) and gradient diversity. Finally, we
provide alternative frameworks that might be better suited to exploit client
sampling techniques like IS and gradient diversity
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