Optimistic Verifiable Training by Controlling Hardware Nondeterminism

• URL: http://arxiv.org/abs/2403.09603v3
• Date: Mon, 25 Nov 2024 09:13:47 GMT
• Title: Optimistic Verifiable Training by Controlling Hardware Nondeterminism
• Authors: Megha Srivastava, Simran Arora, Dan Boneh,
• Abstract summary: Nondeterminism between GPU types during training prevents exact replication of the training process, resulting in schemes that are non-robust. We propose a method that combines training in a higher precision than the target, rounding after intermediate computations, and sharing rounding decisions based on an adaptive thresholding procedure. Our verifiable training scheme significantly decreases the storage and time costs compared to proof-based systems.
• Score: 22.85808027490485
• License:
• Abstract: The increasing compute demands of AI systems have led to the emergence of services that train models on behalf of clients lacking necessary resources. However, ensuring correctness of training and guarding against potential training-time attacks, such as data poisoning and backdoors, poses challenges. Existing works on verifiable training largely fall into two classes: proof-based systems, which are difficult to scale, and ``optimistic'' methods that consider a third-party auditor who can replicate the training process and contest the trainer. A key challenge with the latter is that nondeterminism between GPU types during training prevents exact replication of the training process, resulting in schemes that are non-robust. We propose a method that combines training in a higher precision than the target, rounding after intermediate computations, and sharing rounding decisions based on an adaptive thresholding procedure, to successfully control for nondeterminism. Across three different NVIDIA GPUs (A40, Titan XP, RTX 2080 Ti), we achieve exact training replication at FP32 precision for both full-training and fine-tuning of ResNet-50 (23M) and GPT-2 (117M) models. Our verifiable training scheme significantly decreases the storage and time costs compared to proof-based systems, and is publicly released at https://github.com/meghabyte/verifiable-training.

Related papers

• Always-Sparse Training by Growing Connections with Guided Stochastic Exploration [46.4179239171213]
  We propose an efficient always-sparse training algorithm with excellent scaling to larger and sparser models. We evaluate our method on CIFAR-10/100 and ImageNet using VGG, and ViT models, and compare it against a range of sparsification methods.
  arXiv  Detail & Related papers  (2024-01-12T21:32:04Z)
• Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening [51.34904967046097]
  Selective Synaptic Dampening (SSD) is a fast, performant, and does not require long-term storage of the training data. We present a novel two-step, post hoc, retrain-free approach to machine unlearning which is fast, performant, and does not require long-term storage of the training data.
  arXiv  Detail & Related papers  (2023-08-15T11:30:45Z)
• No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models [31.080446886440757]
  In this work, we revisit three categories of such algorithms: dynamic architectures (layer stacking, dropping), batch selection (selective backprop, RHO loss), and efficient layers (Lion, Sophia) We find that their training, validation, and downstream gains vanish compared to a baseline with a fully-decayed learning rate. We define an evaluation protocol that enables machines to be done on arbitrary computation by mapping all computation time to a reference machine which we call reference system time.
  arXiv  Detail & Related papers  (2023-07-12T20:10:14Z)
• GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks [73.88590165742721]
  We propose a novel adversarial training technique that exploits auxiliary tasks under a limited set of training data. Our approach extends single-task models into multi-task models during the min-max optimization of adversarial training. We demonstrate that guided multi-task learning is an actionable and promising avenue to push further the boundaries of model robustness.
  arXiv  Detail & Related papers  (2023-02-06T16:23:24Z)
• Adversarial Coreset Selection for Efficient Robust Training [11.510009152620666]
  We show how selecting a small subset of training data provides a principled approach to reducing the time complexity of robust training. We conduct extensive experiments to demonstrate that our approach speeds up adversarial training by 2-3 times.
  arXiv  Detail & Related papers  (2022-09-13T07:37:53Z)
• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale [100.19539096465101]
  Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training. We propose a large-batch adversarial training framework implemented over multiple machines.
  arXiv  Detail & Related papers  (2022-06-13T15:39:43Z)
• Self-Progressing Robust Training [146.8337017922058]
  Current robust training methods such as adversarial training explicitly uses an "attack" to generate adversarial examples. We propose a new framework called SPROUT, self-progressing robust training. Our results shed new light on scalable, effective and attack-independent robust training methods.
  arXiv  Detail & Related papers  (2020-12-22T00:45:24Z)
• Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free [115.81899803240758]
  Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. This paper asks how to quickly calibrate a trained model in-situ, to examine the achievable trade-offs between its standard and robust accuracies. Our proposed framework, Once-for-all Adversarial Training (OAT), is built on an innovative model-conditional training framework.
  arXiv  Detail & Related papers  (2020-10-22T16:06:34Z)
• Predicting Training Time Without Training [120.92623395389255]
  We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. We leverage the fact that the training dynamics of a deep network during fine-tuning are well approximated by those of a linearized model. We are able to predict the time it takes to fine-tune a model to a given loss without having to perform any training.
  arXiv  Detail & Related papers  (2020-08-28T04:29:54Z)
• Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs [13.83645579871775]
  Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks. This work pushes the boundary of quantised training by employing a multilevel approach that utilises multiple precisions. MuPPET achieves the same accuracy as standard full-precision training with training-time speedup of up to 1.84$times$ and an average speedup of 1.58$times$ across the networks.
  arXiv  Detail & Related papers  (2020-06-16T10:14:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.