Decentralized Learning in Healthcare: A Review of Emerging Techniques
Bibliographic record
Abstract
Recent developments in deep learning have contributed to numerous success stories in healthcare. The performance of a deep learning model generally improves with the size of the training data. However, there are privacy, ownership, and regulatory issues that prevent combining medical data into traditional centralized storage. Decentralized learning approaches enable collaborative model training by distributing the learning process among several nodes or devices. Conceptually, decentralized learning builds on earlier work in distributed optimization, but the focus of this paper is on recent and emerging techniques such as Federated Learning (FL), Split Learning (SL), and hybrid Split-Federated Learning (SFL). With common, universal deep learning models and centralized aggregator servers, FL overcomes the difficulties of centralized training. Additionally, patient data remains at the local party, upholding the security and anonymity of the data. SL enables machine learning without directly accessing data on clients or end devices. It further enhances privacy in a decentralized setting and mitigates clients’ storage issues. In this survey, we first provide a contemporary survey of FL, SL, and SFL approaches. Second, we discuss their state-of-the-art applications in healthcare, particularly in medical image analysis. Third, we review these emerging decentralized learning approaches under challenging conditions such as statistical and system heterogeneity, privacy preservation, communication efficiency, fairness, etc. Then, we address existing approaches to tackle these challenges. We detail unique complications related to healthcare applications including data, privacy and security, and communication challenges. Finally, we outline potential areas for further research on emerging decentralized learning techniques in healthcare, including developing personalized models, reducing bias, incorporating hybrid non-IID features, hyperparameter tuning, developing sufficient incentive mechanisms, and incorporating domain expertise knowledge.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".