Source-linked AI summary
Distributed learning of deep neural network over multiple agents
Otkrist Gupta, Ramesh Raskar
TL;DR
Sensitive domains may lack sufficient labeled data and computational resources, while privacy constraints limit direct data sharing. The paper proposes distributed deep-neural-network training across multiple entities and a computational resource, and reports results identical to standard training procedures. It also addresses raw-data protection and discusses constraints in existing secure or feature-sharing approaches.
Problem
Sensitive applications face limited labeled data and computational resources, while privacy obligations restrict sharing data across entities.
Method
The paper trains deep neural networks across several data repositories and a computational resource without revealing actual raw data.
Results
The algorithm produces identical results to standard training procedures.
Takeaways & Limitations
The approach supports distributed neural-network training when entities cannot directly share raw labeled data.
Takeaways & Limitations
Oblivious-transfer approaches may be impractical for large-scale datasets because of resource requirements, while shared feature vectors can be accurately inverted.
Abstract
from arXiv · showhide
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.
1. Introduction
Deep neural networks require substantial data, computation, and engineering oversight, while privacy obligations can prevent entities from sharing sensitive data. The paper proposes distributed training across multiple data sources and a shared supercomputing resource without directly sharing raw labeled data.
- The motivation includes applying deep learning to high-dimensional biomedical and health data for prediction and inference.
- Deep neural networks may require large datasets, millions of parameters, and substantial computing power, making training difficult for individual repositories.
- Privacy and ethical concerns surrounding de-anonymized data can make sensitive datasets difficult to use in machine-learning pipelines.
- The proposed methods train neural networks using multiple data sources and a single supercomputing resource.
2. Related Work
Prior work addresses distributed and secure neural-network computation through optimization, feature sharing, cryptographic protocols, and multi-party methods. The paper positions its approach as distributed training across separate data sources while highlighting scalability and privacy limitations in existing techniques.
- Distributed training research includes gradient optimization, delayed-update online learning, kernel simplification, multi-machine training, and multi-GPU computation.
- The paper proposes combining data from several different sources for distributed neural-network computing.
- Oblivious-transfer protocols can provide secure computation but may be impractical for large datasets because of resource requirements.
- Sharing SIFT and HOG features instead of raw data can expose information because feature vectors may be accurately inverted using prior knowledge.
- Neural networks can denoise, reconstruct, or recover images from partial inputs, limiting the protection offered by simple obfuscation.
- Secure distributed processing encompasses data, neural activations, parameters, and broader multi-party computation settings.
3. Theory
The paper targets distributed neural-network training across multiple data entities and one supercomputing resource while reducing the need to share raw labeled data directly. Its stated requirements preserve data locality and constrain how information and parameters are shared.
- The proposed techniques train deep neural networks over multiple data sources while mitigating direct sharing of raw labeled data.
- The problem involves several data entities, called Alices, training with one supercomputing resource, called Bob.
- A data entity does not need to share its data with Bob or other data resources.
2. The supercomputing resource (Bob) wants control over the architecture of the Neural Network(s)
The training design partitions computation between data entities and Bob, who controls later network layers and exchanges encoded activations and gradients. It supports centralized or peer-to-peer participation, and the paper argues that the resulting updates match standard training.
- Architecture and modalities: Bob retains part of the network parameters required for inference while coordinating training with data entities.
- Architecture and modalities: Data can be encoded into another space and transmitted for training, with third parties later using the network for classification and inference.
- Architecture and modalities: The algorithm supports one or multiple data entities in either peer-to-peer or centralized mode.
- Two-agent training: Alice performs forward propagation on local data and sends an intermediate output and label to Bob, who computes losses and gradients.
- Results: The paper concludes that distributed training produces identical results to a normal training procedure.
- Two-agent training: Gradients computed by Bob are sent back to the data entity for backpropagation and weight updates.
- Theory: Sequential forward computation and chain-rule backpropagation make the distributed procedure functionally equivalent to applying the full network at once.
3.2. Distributed training over multiple entities
The algorithm extends distributed neural-network training across multiple data entities, with peer-to-peer and centralized weight-sharing modalities. Under consistent initialization and data order, it is shown to produce the same network as single-entity training.
- Distributed training over multiple entities: The method extends training to N data entities by using a round-robin distributed learning framework.Each entity is denoted Alice_i, and the algorithm adds a step for incorporating data from multiple entities.
- Weight-sharing modalities: Two modalities distribute updated weights: centralized sharing through a server and peer-to-peer exchange between training entities.Centralized mode uses an encrypted weights file, while peer-to-peer mode connects the current party to the last trained entity.
- Weight-sharing modalities: The final result is identical in centralized and peer-to-peer modalities when both begin with the same weights.The paper states that both modalities initialize the same weights before continued training.
- Correctness: The neural network at iteration k is identical to the network trained by one entity, assuming consistent initialization and preserved data order.The proof uses inductive reasoning and functional equivalence of distributed backpropagation to training over combined data on one machine.
- Correctness: The recursive proof maintains correct weights across iterations because each entity performs backpropagation functionally equivalent to full-network backpropagation.The assertion states that correct weights at iteration i remain correct at iteration i + 1.
3.3. Semi-supervised application
The distributed framework is modified to support autoencoders and semi-supervised learning when labeled data are scarce. Alice computes encoder and decoder-related operations while Bob handles the predictive segment and returns gradients.
- Semi-supervised application: The extension targets semi-supervised learning and generative losses for training with fewer labeled data points.The approach combines predictive and generative objectives when labeled samples are limited.
- Forward and backward propagation: Alice sends encoder outputs to Bob, who processes the predictive layers and computes classifier loss.Bob propagates the feature tensor through its layers and computes logistic-regression loss.
- Loss combination: Alice computes autoencoder gradients through its decoder and combines them with gradients received from Bob.The combined gradients are used for backpropagation over Alice’s network.
- Algorithm 3: The distributed autoencoder procedure has Alice propagate data through its network, receive gradients from Bob, and backpropagate combined losses.This sequence is described as the operational extension for autoencoder and semi-supervised training.
3.4. Online learning
The method also supports online training by sending forward-propagation outputs whenever new annotated data arrive. It reduces network overhead through seeded initialization and can exchange updates or encrypted weights.
- Online learning: Online training provides Bob with forward-propagation output whenever new annotated data become available.This allows training to proceed as annotated data arrive rather than requiring a single batch transfer.
- Communication efficiency: Agents can initialize weights from a shared random seed instead of transmitting the entire neural network.The seed-based initialization is described as preventing further network overhead.
- Communication modalities: In peer-to-peer mode, an agent shares the weight updates added during training rather than full parameters.Combined updates can be computed by subtracting the initial weights from the current weights.
- Security consideration: Encrypted updates are presented as making actual weights harder to reverse engineer during a man-in-the-middle attack.The passage frames encryption as a security measure rather than a complete security analysis.
- Communication modalities: A centralized weight server can distribute encrypted weights and receive encrypted updated weights after training.The centralized procedure modifies the weight-download step and uploads new encrypted weights when training ends.
3.5. Analyzing Security Concerns
The security discussion argues that reconstructing Alice’s parameters is difficult when the transmitted network section contains a fully connected layer. It also notes that label sharing remains a separate privacy issue addressed by a topology modification.
- Parameter reconstruction: The security argument assumes Alice’s network section contains at least one fully connected layer.Under this condition, the paper argues that Bob cannot discover Alice’s parameters.
- Parameter reconstruction: A fully connected layer with N outputs has at least N! functionally equivalent configurations created by permuting output neurons.These configurations preserve the layer’s function while changing its topology representation.
- Computational security: Bob must examine at least N! configurations to invert the transformation and recover Alice’s parameters.The paper bounds this search by N! > (N/2)^N > e^N, making the required time exponential in layer size N.
- Computational security: For a 4096-neuron fully connected layer, testing one configuration per second would exceed the current age of the universe.This example illustrates the paper’s claimed scale of the configuration-search barrier.
- Training without label sharing: The algorithm does not require raw-data sharing but initially still involves label sharing.The paper proposes wrapping the network around its end layers so Alice can generate gradients without sharing labels.
- Training without label sharing: The wrapped topology preserves the training method because Alice receives end-layer gradients and backpropagates them through her own network.The authors state that the method continues to work after the layers are wrapped around.
4. Datasets and Implementation
The implementation uses asynchronous RPC over SSL with three network-primitive categories, and evaluates distributed training on MNIST, CIFAR variants, and ImageNet-scale data.
- Implementation: The protocol uses standard JSON libraries for asynchronous RPC over an SSL-secured connection.
- Implementation: Network primitives are organized into training requests, tensor transmission, and weight updates.
- Datasets: MNIST contains 70,000 handwritten-digit samples, divided into 60,000 training and 10,000 testing samples.
- Datasets: CIFAR-10 contains 60,000 32 × 32 color images across 10 classes, with 50,000 training and 10,000 testing images.
- Datasets: The ImageNet-derived dataset includes approximately 1.2 million images labeled across 1000 object categories, plus 150,000 validation and testing images.
5. Experiments and Applications
Experiments test the method across datasets, network topologies, client counts, and comparisons with federated methods. The results report similar distributed and single-machine accuracies alongside lower client computation and communication costs.
- Accuracy validation: The method produces identical results across multiple agents and different topologies under equal epoch counts.
- Experimental setup: The implementation is evaluated on datasets and topologies including MNIST, ILSVRC 12, and CIFAR-10.
- Accuracy validation: Distributed training converges to similar accuracies as single-machine training across datasets ranging from 50K to 1M samples and 10, 100, or 1000 classes.
- Comparison with existing methods: The method achieves higher validation accuracy and faster convergence with a large number of clients than the compared methods.
- Comparison with existing methods: The method reduces computation and communication bandwidth relative to federated SGD and federated averaging.
- Comparison with existing methods: Client computation is reduced because forward-pass and gradient calculations apply only to the first few layers rather than the entire network.
- Comparison with existing methods: Communication is reduced by restricting the client-side network to the first few layers, while federated averaging transmits full-network gradients and weights.
- Multiple data sources: Adding agents to pooled datasets significantly improves accuracy when data are divided uniformly across 10 agents.
6. Conclusions and Future Work
The paper presents distributed deep-network training across multiple repositories and computational resources without revealing raw data, with extensions for low-label settings, privacy, and other task types.
- It provides algorithms for training without revealing actual raw data and supplies mathematical correctness guarantees.
- The distributed setup can reduce computation on individual data sources while keeping proprietary or biomedical data from being directly shared.
- The algorithm is validated on CIFAR-10 and ILSVRC12, producing identical results to standard training procedures.
- The approach combines data from several sources in low-data scenarios and extends to semi-supervised learning with fewer labeled samples.
- The method trains a single network topology over several data repositories and a computational resource.
- Future directions include ensemble training, student-teacher transfer, Tor-like layer distribution, and applications to regression, segmentation, LSTMs, and recurrent networks.