Blockchain-Based Decentralized Federated Learning for Secure AI Model Training
Bibliographic record
Abstract
With the rapid growth of Artificial Intelligence (AI) and machine learning models, the demand for large-scale data and computing resources has surged. However, this centralized approach to training AI models raises significant concerns about data privacy, security, and resource management. In this paper, we propose a Blockchain-Based Decentralized Federated Learning (BC-DFL) framework to address these challenges while ensuring privacy, security, and fairness in AI model training. The BC-DFL framework leverages blockchain technology to create a decentralized, transparent, and secure environment for collaborative AI model training, where data remains on local devices, and only model updates are shared. Federated Learning Integration: A decentralized approach to training machine learning models that preserves data privacy by ensuring that data never leaves the local device.Blockchain for Security and Transparency: Blockchain is used to securely aggregate model updates, verify authenticity, and ensure transparency in the training process. Smart contracts are employed to enforce privacy policies and incentivize participants.Decentralization: Unlike traditional centralized systems, BC-DFL eliminates the need for a central server, distributing both computational load and model training across multiple nodes. We evaluate the performance of BC-DFL in comparison with traditional centralized federated learning frameworks. Our experiments, conducted on a set of benchmark datasets, demonstrate that BC-DFL achieves 85% model accuracy, with 20% improved privacy due to decentralized training. Moreover, it ensures 100% traceability of model updates and maintains near-zero data leakage between participating nodes.This work demonstrates the potential of combining blockchain with federated learning to develop secure, efficient, and scalable AI models, suitable for environments where privacy and data security are paramount..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".