Federated Learning for Data Trading Portfolio Allocation With Autonomous Economic Agents
Bibliographic record
Abstract
In the rapidly advancing ubiquitous intelligence society, the role of data as a valuable resource has become paramount. As a result, there is a growing need for the development of autonomous economic agents (AEAs) capable of intelligently and autonomously trading data. These AEAs are responsible for acquiring, processing, and selling data to entities such as software companies. To ensure optimal profitability, an intelligent AEA must carefully allocate its portfolio, relying on accurate return estimation and well-designed models. However, a significant challenge arises due to the sensitive and confidential nature of data trading. Each AEA possesses only limited local information, which may not be sufficient for training a robust and effective portfolio allocation model. To address this limitation, we propose a novel data trading market where AEAs exclusively possess local market information. To overcome the information constraint, AEAs employ federated learning (FL) that allows multiple AEAs to jointly train a model capable of generating promising portfolio allocations for multiple data products. To account for the dynamic and ever-changing revenue returns, we introduce an integration of the histogram of oriented gradients (HoGs) with the discrete wavelet transformation (DWT). This innovative combination serves to redefine the representation of local market information to effectively handle the inherent nonstationarity of revenue patterns associated with data products. Furthermore, we leverage the transform domain of local model drifts in the global model update process, effectively reducing the communication burden and significantly improving training efficiency. Through simulations, we provide compelling evidence that our proposed schemes deliver superior performance across multiple evaluation metrics, including test loss, cumulative return, portfolio risk, and Sharpe ratio.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".