Distributed Adversarial Training With Gradient Aggregation at Scale
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Solution Overview
Problem
Existing adversarial training techniques for deep neural networks struggle to scale effectively to large networks and datasets, leading to inefficiencies in training time and performance when faced with adversarial perturbations.
Innovation Solution
A distributed adversarial training method that leverages multiple computing machines to generate and aggregate gradients, allowing for large-batch optimization and efficient communication of compressed gradients using layer-wise adaptive learning rates, thereby enhancing scalability and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is applied to deep neural networks, then robustness against adversarial perturbations is improved, but training time and computational resources increase significantly
Solution Approach 1:
The patent divides the training process into multiple independent worker nodes that each handle portions of the dataset and model parameters. This segmentation allows parallel processing of adversarial training examples across multiple machines, reducing overall training time while maintaining robustness through coordinated gradient updates aggregated by a parameter server.
Solution Approach 2:
The patent introduces a distributed computing dimension by deploying training across multiple machines with different configurations. This enables the system to process larger batches of adversarial examples simultaneously, achieving faster training convergence without sacrificing the robustness guarantees provided by adversarial training.
2Reliability
If adversarial training is applied to deep neural networks, then robustness against adversarial perturbations is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent designs worker nodes that can operate with different model architectures and dataset configurations using a unified distributed training framework. This multi-functionality allows the same infrastructure to handle various deep learning tasks while maintaining adversarial training capabilities, reducing the need for specialized hardware configurations.
Solution Approach 2:
The parameter server acts as an intermediary that coordinates gradient exchanges between worker nodes and aggregates updates centrally. This mediator simplifies the distributed infrastructure by providing a centralized coordination point, reducing the complexity of peer-to-peer communication and synchronization that would otherwise be required across multiple machines.
3Productivity
If larger batch sizes are used in distributed training, then training speed is improved, but communication overhead between machines increases
Solution Approach 1:
The patent segments the computation and communication tasks by having worker nodes process local batches independently before aggregating gradients at the parameter server. This segmentation allows larger effective batch sizes to be processed in parallel while minimizing communication frequency, as each worker only needs to exchange gradient information with the server at specific update intervals.
Solution Approach 2:
The patent implements partial synchronization where not all worker nodes communicate with the parameter server at every iteration. Instead, updates are aggregated periodically or based on convergence criteria, reducing communication overhead while still achieving the benefits of large batch processing for improved training speed.
Data Source
AI summary
Scalable distributed adversarial training techniques for robust deep neural networks are provided. In one aspect, a method for adversarial training of a deep neural network-based model by distributed computing machines M includes, by distributed computing machines M: obtaining adversarial perturbation-modified training examples for samples in a local dataset D(i); computing gradients of a local cost function ƒi with respect to parameters θ of the deep neural network-based model using the adversarial perturbation-modified training examples; transmitting the gradients of the local cost function ƒi to a server which aggregates the gradients of the local cost function ƒi and transmits an aggregated gradient to the distributed computing machines M; and updating the parameters θ of the deep neural network-based model stored at each of the distributed computing machines M based on the aggregated gradient received from the server. A method for distributed adversarial training of a deep neural network-based model by the server is also provided.