BFLMeta: Blockchain-Empowered Metaverse with Byzantine-Robust Federated Learning
Bibliographic record
Abstract
The emerging metaverse is envisioned as a virtual mapping of the real world, thus it would inevitably employ numerous Machine Learning (ML) frameworks to analyze and process massive data for the virtual-physical synchronization process. As a distributed ML paradigm, Federated Learning (FL) can naturally take advantage of numerous IoT, wearable devices, and edge, cloud servers under the metaverse infrastructure to train ML models with privacy guarantee. However, the large-scale and decentralized nature of the metaverse can pose significant challenges to traditional FL schemes, where there is a centralized server aggregating the local models received from local devices. It is not only vulnerable to Single Point of Failure (SPoF), but also lacks incentive mechanisms encouraging metaverse users to contribute their resources and data. In this paper, we propose BFLMeta, a blockchain-based FL scheme for the metaverse in which the aggregation process is performed in a decentralized manner, while the framework can estimate the non-IID degree of data to flexibly adjust blockchain committee size, thereby mitigating the impact of malicious aggregators. Security analysis shows that BFLMeta can resist SPoF, poisoning attack, privacy leakage, and sybil attack. Besides, our evaluation on computation, communication, and performance illustrates the efficiency of BFLMeta. Notably, BFLMeta can converge even with more than 50% poisoning nodes.
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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 | 0.003 | 0.007 |
| 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 | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".