Distributed learning of deep neural network over multiple agents (1810.06060v1)

Abstract: In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.

• The paper presents a novel framework that enables collaborative DNN training without sharing raw data, ensuring strong privacy safeguards.
• It employs a segmented network approach, distributing layers and gradient computations between multiple agents and a central node to optimize resource use.
• Experimental results on datasets like MNIST, CIFAR-10, and ILSVRC 2012 demonstrate comparable accuracy to centralized methods, confirming the method's scalability.

Distributed Learning of Deep Neural Networks Over Multiple Agents

The paper explores a technique for the distributed training of deep neural networks (DNNs) over multiple data sources, aimed at addressing challenges posed by data scarcity and computational constraints in domains such as healthcare and finance. This approach facilitates the collaborative training of neural networks without necessitating the direct sharing of raw data, thus safeguarding data privacy and addressing ethical concerns.

Problem Context and Objectives

Training deep neural networks requires vast amounts of labeled data and significant computational resources, posing a challenge for individual data entities constrained by privacy and resource limitations. The paper proposes a distributed learning methodology that enables multiple entities to collaboratively train DNNs while retaining control over their data. This setup involves a central computation resource guiding the training, thus offloading the computational burden from individual data entities.

Methodology

The proposed method distributes the training process across multiple agents. Each agent processes its data through a portion of the DNN and sends intermediate representations to a centralized node, which performs the remaining computations. Key components of the method include:

1. Layer Segmentation: The DNN is segmented into sub-networks handled by different agents and the central node, enabling distributed computation.
2. Backpropagation: Gradient computations are likewise distributed, ensuring updates across the entire network structure without necessitating full data sharing.
3. Security Concerns: Measures are articulated to mitigate potential reconstruction attacks on transmitted representations, asserting robust privacy protections through network design nuances like layer permutations.

Experimental Evaluation

The efficacy of this distributed training approach is validated using well-known datasets like MNIST, CIFAR-10, and ILSVRC 2012. The experiments show that the distributed methodology achieves comparable classification accuracies to traditional, centralized training approaches. The paper demonstrates improved data utility across agents, highlighting the value of combining datasets without compromising privacy.

Theoretical and Practical Implications

The proposed technique presents profound implications:

• Privacy Preservation: By avoiding raw data sharing, the method significantly improves data privacy, which is crucial for sensitive applications in healthcare and finance.
• Computational Efficiency: By leveraging distributed resources, the approach reduces the computational burden on individual agents, making it feasible for entities with limited resources to participate in model training.
• Scalability: Effectively training models in a distributed fashion allows for scalability in both data volume and model complexity.

Future Directions

Potential areas for further development include:

• Refinement of Security Analysis: While the paper provides an initial assessment, in-depth formal security verification would enhance trust in the method.
• Extension to Diverse Models: The methodology could be adapted to other architectures, such as RNNs and LSTMs, broadening its applicability.
• Real-World Deployment: Pilot deployments in domains with stringent privacy needs would validate the approach outside theoretical or experimental settings.

In summary, this work contributes a viable framework for distributed neural network training that respects data privacy and optimizes resource utilization, paving the way for broader adoption of machine learning in privacy-sensitive industries.