Cooperation-Based Federated Learning and Communication Optimization Under Intermittent Device Participation in Industrial IoT
Bibliographic record
Abstract
In this paper, we propose a novel cooperative relay-based resource and learning optimization (CRRLO) scheme that extends device connectivity, balances learning contributions, and coordinates communication resources to mitigate the negative impact of intermittent participation on FL performance caused by unreliable communication in Industrial Internet of Things (IIoT) environments. After local training, a cooperative aggregation stage is proposed, where fully connected device-to-device (D2D) relaying enables devices with failed device-to-server (D2S) transmissions to still contribute to the global model, while avoiding the transmission burden and relay selection issues associated with single-relay strategies. To further ensure unbiased aggregation, we produce reliability-driven aggregation weights to calibrate each device’s contribution to the global update. We then formulate a joint optimization problem aimed at improving FL convergence rate under communication and resource constraints by co-optimizing blocklength, transmission power, and aggregation weights. An iterative algorithm is designed to determine blocklength bounds, a low-complexity method is developed for power optimization, and a convex relaxation approach is adopted for weight adjustment. These subproblems are alternately solved using a block coordinate descent (BCD) method. Simulation results demonstrate that the proposed CRRLO scheme significantly accelerates convergence and improves test accuracy by up to 24.33% compared to baseline schemes under high transmission error probability.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".