Proof-of-Learning: Definitions and Practice (2103.05633v1)

Abstract: Training ML models typically involves expensive iterative optimization. Once the model's final parameters are released, there is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. Such a mechanism would support security of ML applications in several ways. For instance, it would simplify ownership resolution when multiple parties contest ownership of a specific model. It would also facilitate the distributed training across untrusted workers where Byzantine workers might otherwise mount a denial-of-service by returning incorrect model updates. In this paper, we remediate this problem by introducing the concept of proof-of-learning in ML. Inspired by research on both proof-of-work and verified computations, we observe how a seminal training algorithm, stochastic gradient descent, accumulates secret information due to its stochasticity. This produces a natural construction for a proof-of-learning which demonstrates that a party has expended the compute require to obtain a set of model parameters correctly. In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform at least as much work than is needed for gradient descent itself. We also instantiate a concrete proof-of-learning mechanism in both of the scenarios described above. In model ownership resolution, it protects the intellectual property of models released publicly. In distributed training, it preserves availability of the training procedure. Our empirical evaluation validates that our proof-of-learning mechanism is robust to variance induced by the hardware (ML accelerators) and software stacks.

• The paper presents a novel Proof-of-Learning mechanism that utilizes periodic checkpoints in SGD to verify the integrity of ML model training.
• It employs cryptographic signatures and the inherent randomness of SGD to detect adversarial spoofing with high confidence.
• The approach lays groundwork for robust ownership and trust verification in distributed and outsourced ML training setups.

Proof-of-Learning for Machine Learning Model Verification

The concept of "Proof-of-Learning" (PoL) presented in the paper aims to address the lack of mechanisms to verify the integrity and authenticity of ML model training processes. This concept is particularly vital in scenarios where the ownership of a model is disputed or when model training is outsourced to untrusted parties in a distributed setup, where verification of the training completion is crucial.

The authors introduce a PoL strategy inspired by cryptographic proofs-of-work and verified computations. It focuses on the stochastic gradient descent (SGD) process utilized during ML training as a means to demonstrate computational effort. This approach posits that the inherent randomness of SGD generates secret information that can be leveraged to prove that the computational resources required to train a model were genuinely expended.

Core Proposal

1. Proof-of-Learning Mechanism: The proposed mechanism requires checkpoints to be recorded periodically during the training process. Each checkpoint consists of model weights, batch indices, signatures of the data points used, and metadata such as hyperparameters. The prover encrypts this PoL and publishes it along with a signature. This setup ensures that any attempt by an adversary to spoof the PoL can be detected with high probability, given the intrinsic entropy growth in SGD.
2. Verification Process: The verifier assesses the PoL by verifying the randomness of the initial state and checking intermediate weights through recalculation and comparison within a specified tolerance. This process finds any discrepancies, especially those introduced by adversarial attempts to create structurally correct yet invalid PoL.
3. Security Analysis: The paper details the security landscape, examining retraining-based and inverse gradient methods as potential threats. It analytically demonstrates that spoofing efforts require equal or greater computational effort than honest proof creation, ensuring the robustness of the verification against dishonest attempts.

Implications and Future Work

The methodology proposed provides a structured approach to address significant challenges in ML security, such as model extraction and Byzantine faults in distributed training scenarios. By introducing PoL, the authors open new avenues for ML model verification, where trust and ownership can be cryptographically certified without drastic changes to existing training procedures.

The theoretical underpinnings offer hypotheses regarding entropy growth in training processes, which warrant further experimental verification across various architectures beyond DNNs. Additionally, exploring efficient cryptographic primitives like fuzzy extractors or locality-sensitive hashing could enhance the efficacy of PoL with respect to storage and verification costs.

Advancements in this field could lead to PoL systems becoming standardized in ML infrastructures, ensuring integrity, trust, and ownership verification for commercially sensitive models, thus safeguarding intellectual property and operational integrity. Further refinements could address private dataset usage and computational overheads, particularly in resource-constrained environments.

Overall, the proposal of PoL introduces a promising paradigm shift in addressing security risks associated with ML models and distributed computing, paving the way for securing AI technologies amidst increasing adversarial threats.