FedRSMax: An Effective Aggregation Technique for Federated Learning with Medical Images
Bibliographic record
Abstract
The traditional deep learning framework faces two critical challenges: limited data available for successful model training and concerns regarding user data privacy. Federated learning, which operates in a decentralised paradigm, offers a promising solution to these challenges. Federated averaging (FedAvg) is a common aggregation procedure in federated contexts. FedAvg, however, experiences convergence issues, especially when there is significant diversity in the data distributions among clients. To address this problem, we explore two effective aggregation techniques, namely random-sampling federated maximum (FedRSMax) and random-sampling federated median (FedRSMed) with adaptive moment estimation (Adam), and compare their performance characteristics with FedAvg. In this study, we use a well-established convolutional neural network (LeNet) as a global model for federated learning, and the HAM10000 dermatoscopic image dataset is used as the primary data source. We balance the dataset and generate random subsets to induce data heterogeneity for different simulated clients and evaluate the performance of the proposed techniques. Our findings demonstrate that FedRSMax outperforms FedRSMed and FedAvg in terms of accuracy, recall, and precision and can therefore serve as an effective alternative for aggregation in federated learning.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".