Bias in Federated Learning: Factors, Effects, Mitigations, and Open Issues
Bibliographic record
Abstract
Federated learning (FL) enables collaborative model training from decentralized data while preserving privacy.However, biases manifest due to sample selection, population drift, locally biased data, societal issues, algorithmic assumptions, and representation choices.These biases accumulate in FL models, causing unfairness.Tailored detection and mitigation methods are needed.This paper analyzes sources of bias unique to FL, their effects, and specialized mitigation strategies like robust aggregation, cryptographic protocols, and algorithmic debiasing.We categorize techniques and discuss open challenges around miscoordination, privacy constraints, decentralized evaluation, data poisoning attacks, systems heterogeneity, incentive misalignments, personalization tradeoffs, emerging governance needs, and participation.As FL expands into critical domains, ensuring equitable access without ingrained biases is imperative.This study provides a conceptual foundation for future research on developing accurate, robust and fair FL through tailored technical solutions and participatory approaches attuned to the decentralized environment.It aims to motivate further work toward trustworthy and inclusive FL.
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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 | 0.046 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".