Reputation-enabled Federated Learning Model Aggregation in Mobile Platforms
Bibliographic record
Abstract
Federated Learning (FL) builds on a mobile network of participating nodes that train local models and contribute to the learning model parameters at a central server without being obliged to share their raw data. The server aggregates the uploaded model parameters to generate a global model. Common practice for the uploaded local models is an evenly weighted aggregation, assuming that each node of the network contributes to advancing the global model equally. Due to the heterogeneous nature of the devices and collected data, it is inevitable to have variations between the contributions of the users to the global model. Therefore, users (i.e., devices) with higher contributions should be weighted higher during aggregation. With this in mind, this paper proposes a reputation-enabled aggregation methodology that scales the aggregation weights of users by their reputation scores. Reputation score of a user is computed according to the performance metrics of their trained local models during each training round, therefore it can be a metric to evaluate the direct contributions of their trained local model. Numerical comparison of the proposed aggregation methodology to a baseline that utilizes standard averaging as well as a second baseline that is scoped to a reputation-based client selection shows an improvement of 17.175% over the standard baseline for not independent and identically distributed (non-IID) scenarios for an FL network of 100 participants. Consistent improvements over the first and second baselines under smaller FL networks with users ranging from 20 to 100 are also shown.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".