Clustered Federated Learning with Non-IID Data: Mitigating Accuracy Overestimates Through Hold-Out Model Selection and Evaluation
Bibliographic record
Abstract
Federated learning (FL), a distributed learning strategy, improves security and privacy by eliminating the need for clients to share their local data; however, FL struggles with non-IID (non independent and identically distributed) data. Clustered FL aims to remedy this by grouping similar clients and training a model per group; nevertheless, it faces difficulties in determining clusters without sharing local data and conducting model evaluation. Clustered FL evaluation on unseen clients typically applies all models, selecting the best-performer for each client - approach known as best-fit cluster evaluation. This paper challenges such evaluation process arguing that it violates a fundamental machine learning principle: test dataset labels should be used only for performance calculation, not for model selection. We show that best-fit cluster evaluation results in significant accuracy overestimates. Moreover, we present an evaluation approach that maintains the separation between model selection and evaluation by reserving a portion of the target client data for model selection, while the remaining data is used for accuracy estimation. Experiments on four datasets, encompassing various IID and non-IID scenarios, demonstrate that the best-fit cluster evaluation produces overestimates that are statistically different from our evaluation.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".