Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework
Bibliographic record
Abstract
Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".