Fed-IT: Addressing Class Imbalance in Federated Learning through an Information- Theoretic Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Federated learning (FL) is a promising technology wherein edge devices/clients collaboratively train a machine learning model under the orchestration of a central server. However, due to the inherent data heterogeneity among clients, local datasets on individual clients often exhibit class imbalance, i.e., samples from majority classes vastly outnumber those from minority classes. This imbalance significantly diminishes the performance of the trained model. To understand why, we first closely examine the output probability distribution clusters of the local deep neural networks (DNNs) in the probability space over the label set, and observe that for class imbalanced datasets, FL has two interesting phenomena: (1) dispersion problem-clusters corresponding to minority classes tend to disperse; and (2) gravity problem-clusters corresponding to minority classes are drawn toward those of majority classes. To overcome these two problems, we then introduce information quantities into FL, propose a new information theoretic loss function for FL, and develop a new FL framework called Fed-IT. It is shown that Fed-IT significantly outperforms previous counterparts, while maintaining client privacy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it