Reputation-Based Model Aggregation and Resource Optimization in Wireless Federated Learning Systems
Bibliographic record
Abstract
Federated learning (FL) has received widespread attention from academia and industry because it overcomes traditional security limitations associated with model training data. However, the FL process is vulnerable to manipulation by locally malicious users, who can alter their local data, thus impacting the accuracy of the model’s training outcomes. Meanwhile, optimizing delay in FL needs to take individual client fairness into consideration. In this paper, we present a reputation-based model aggregation and resource optimization framework to enhance the efficiency and reliability of training in wireless FL systems. Particularly, we investigate a total delay minimization problem while ensuring fairness among clients, which jointly optimizes client scheduling, transmit rate, bandwidth proportion, and CPU frequency. Considering the non-convexity and high complexity of the objective function, we decoupled the optimal variables and designed an efficient algorithm. By doing this, the client scheduling policy is obtained by deep reinforcement learning. Then, the transmit rate allocation and bandwidth proportion are derived through the Lagrangian dual method. Finally, we attain the CPU frequency allocation via the adaptive harmony algorithm. Simulation results reveal that our algorithm can establish delay fairness among clients and balance convergence performance and delay.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.007 | 0.000 |
| 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".