FedRL: Improving the Performance of Federated Learning with Non-IID Data
Bibliographic record
Abstract
Federated learning preserves data privacy by training machine learning models in a distributed fashion, where local models are trained on the client devices and aggregated on the server. Prevalent aggregation algorithms in federated learning perform well in homogeneous settings, but suffer from inadequate convergence in heterogeneous settings due to non-IID data distribution. In this paper, we explore the shortcomings of existing work and recognize that the memory loss of optimizers in aggregation steps limits convergence performance. In response, we propose FedRL, a new adaptive aggregation algorithm with the supervision of a policy-based deep reinforcement learning agent. Using real-world datasets, we evaluate the effectiveness of FedRL by comparing to state-of-the-art adaptive aggregation algorithms in the literature, and show its superiority in accelerating convergence to a target accuracy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.104 | 0.239 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".