Communication-Efficient Federated Learning for Large-Scale Multiagent Systems in ISAC: Data Augmentation With Reinforcement Learning
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) has attracted great attention with the gains of spectrum efficiency and deployment costs through the coexistence of sensing and communication functions. Meanwhile, federated learning (FL) has great potential to apply to large-scale multiagent systems (LSMAS) in ISAC due to the attractive privacy protection mechanism. Nonindependent identically distribution (non-IID) is a fundamental challenge in FL and seriously affects the convergence performance. To deal with the non-IID issue in FL, a data augmentation optimization algorithm (DAOA) is proposed based on reinforcement learning (RL), where an augmented dataset is generated based on a generative adversarial network (GAN) and the local model parameters are inputted into a deep Q-network (DQN) to learn the optimal number of augmented data. Different from the existing works that only optimize the training performance, the number of augmented data is also considered to improve the sample efficiency in the article. In addition, to alleviate the high-dimensional input challenge in DQN and reduce the communication overhead in FL, a lightweight model is applied to the client based on deep separable convolution (DSC). Simulation results indicate that our proposed DAOA algorithm acquires considerable performance with significantly fewer augmented data, and the communication overhead is reduced greatly compared with benchmark algorithms.
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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 | 0.005 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".