Proof of Learning: Building Trust in Future Machine Learning

Proof of Learning (PoL) verifies that a model was genuinely trained on claimed data by providing verifiable evidence of the training process. As machine learning systems become pervasive in critical domains, PoL offers a mechanism to ensure trust and accountability in model provenance.

Why PoL Matters

1. Model provenance – PoL links models to their training data and processes, deterring plagiarism and unauthorized reuse.
2. Regulatory compliance – Governments and industries are moving toward regulations that demand auditable machine learning pipelines.
3. Economic incentives – Integrating PoL with blockchain allows useful training work to replace wasteful mining computations [5].

Strengthening PoL

Our research explores model watermarking to protect PoL against spoofing attacks. Feature-based watermarking ties a model to its training data, making forgeries detectable [1]. A follow-up evaluation compares multiple watermarking approaches and analyzes robustness versus computational overhead [2]. The dissertation extends these findings and offers deployment guidelines for secure PoL pipelines [3]. Earlier survey work examines how blockchain mechanisms complement PoL in decentralized learning environments [4].

Future Outlook

Emerging blockchain protocols employ PoL as a form of Proof-of-Useful-Work, demonstrating how verifiable training can secure decentralized networks while advancing machine learning [5,6]. As the demand for trustworthy AI grows, PoL will underpin open model markets, verifiable federated learning, and energy-efficient consensus systems.

References

[1] Ural, O. and Yoshigoe, K. (2024). Enhancing Security of Proof-of-Learning against Spoofing Attacks using Feature-Based Model Watermarking. IEEE Access. [2] Ural, O. and Yoshigoe, K. (2025). Evaluation of Model Watermarking Techniques for Proof-of-Learning Security Against Spoofing Attacks. IEEE Access (in press). [3] Ural, O. (2025). Enhancing Proof-of-Learning Security Against Spoofing Attacks Using Model Watermarking. Doctoral Dissertation, Embry-Riddle Aeronautical University. [4] Ural, O. and Yoshigoe, K. (2023). Survey on Blockchain-Enhanced Machine Learning. IEEE Access. [5] Lan, Y., Liu, Y., and Li, B. (2020). Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains. arXiv:2007.15145. [6] Zhao, Z., Fang, Z., Wang, X., Chen, X., Su, H., Xiao, H., and Zhou, Y. (2024). Proof-of-Learning with Incentive Security. arXiv:2404.09005.