ARXIV:2603.15901 · MEDICAL AI · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Federated Learning for Privacy-Preserving Medical AI

arXiv

A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data. Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking, limiting their practical deployment in healthcare.

METHOD

Full abstract

This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking, limiting their practical deployment in healthcare. To address these gaps, this research proposes a novel site-aware data partitioning strategy that preserves institutional boundaries, reflecting real-world multi-institutional collaborations and data heterogeneity. Furthermore, an Adaptive Local Differential Privacy (ALDP) mechanism is introduced, dynamically adjusting privacy parameters based on training progression and parameter characteristics, thereby significantly improving the privacy-utility trade-off over traditional fixed-noise approaches. Systematic empirical evaluation across multiple client federations and privacy budgets demonstrated that advanced federated optimisation algorithms, particularly FedProx, could equal or surpass centralised training performance while ensuring rigorous privacy protection. Notably, ALDP achieved up to 80.4% accuracy in a two-client configuration, surpassing fixed-noise Local DP by 5-7 percentage points and demonstrating substantially greater training stability. The comprehensive ablation studies and benchmarking establish quantitative standards for privacy-preserving collaborative medical AI, providing practical guidelines for real-world deployment. This work thereby advances the state-of-the-art in federated learning for medical imaging, establishing both methodological foundations and empirical evidence necessary for future privacy-compliant AI adoption in healthcare.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. This work thereby advances the state-of-the-art in federated learning for medical imaging, establishing both methodological foundations and empirical evidence necessary for future privacy-compliant AI…

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainA novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

{ "contract_version": "paper-r2", "paper_id": "132922cb-89e7-42ec-9d0b-149cd7bf9a19", "arxiv_id": "2603.15901", "canonical_route": "/paper/federated-learning-for-privacy-preserving-medical-ai", "active_tab": "synced from current hash by the drawer client", "selected_artifact": "federated-learning-for-privacy-preserving-medical-ai", "endpoints": { "paper_pack": "/api/v1/paper/federated-learning-for-privacy-preserving-medical-ai/paper-pack", "build_passport": "/api/v1/paper/federated-learning-for-privacy-preserving-medical-ai/build-passport", "mcp_resource": "sciencetostartup://surfaces/paper-workspace" } }

{ "surface": "paper", "mode": "paper", "query": "Federated Learning for Privacy-Preserving Medical AI", "normalized_query": "2603.15901", "route": "/paper/federated-learning-for-privacy-preserving-medical-ai", "paper_ref": "federated-learning-for-privacy-preserving-medical-ai", "topic_slug": null, "benchmark_ref": null, "dataset_ref": null }

{ "@context": "https://schema.org", "@graph": [ { "@type": "WebPage", "@id": "https://sciencetostartup.com/paper/federated-learning-for-privacy-preserving-medical-ai#webpage", "url": "https://sciencetostartup.com/paper/federated-learning-for-privacy-preserving-medical-ai", "name": "Federated Learning for Privacy-Preserving Medical AI", "description": "A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.", "isPartOf": { "@id": "https://sciencetostartup.com/#website" } }, { "@type": "ScholarlyArticle", "@id": "https://sciencetostartup.com/paper/federated-learning-for-privacy-preserving-medical-ai#scholarlyArticle", "headline": "Federated Learning for Privacy-Preserving Medical AI", "description": "A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.", "url": "https://sciencetostartup.com/paper/federated-learning-for-privacy-preserving-medical-ai", "sameAs": "https://arxiv.org/abs/2603.15901", "identifier": { "@type": "PropertyValue", "propertyID": "arXiv", "value": "2603.15901" }, "isAccessibleForFree": true, "isPartOf": { "@id": "https://sciencetostartup.com/#website" }, "datePublished": "2026-03-16T20:46:21.000Z", "additionalProperty": [ { "@type": "PropertyValue", "propertyID": "viabilityScore", "value": 6 }, { "@type": "PropertyValue", "propertyID": "researchDomain", "value": "Medical AI" } ] }, { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://sciencetostartup.com" }, { "@type": "ListItem", "position": 2, "name": "Medical AI", "item": "https://sciencetostartup.com/topics" }, { "@type": "ListItem", "position": 3, "name": "Federated Learning for Privacy-Preserving Medical AI", "item": "https://sciencetostartup.com/paper/federated-learning-for-privacy-preserving-medical-ai" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What products could be built from this research?", "acceptedAnswer": { "@type": "Answer", "text": "Now is the time because healthcare AI adoption is accelerating but hitting regulatory walls, privacy regulations are tightening globally, and institutions are increasingly willing to collaborate but need privacy-preserving frameworks. The COVID-19 pandemic demonstrated the value of cross-institutional medical research while highlighting privacy concerns." } }, { "@type": "Question", "name": "What are the practical use cases?", "acceptedAnswer": { "@type": "Answer", "text": "A federated learning platform that enables 10 major hospital systems to collaboratively train an Alzheimer's detection model using their combined MRI data, with each hospital maintaining full control of their patient data while contributing to a shared model that outperforms what any single institution could develop alone." } } ] } ] }

Competitive landscape

A novel federated learning approach for privacy-preserving Alzheimer's disease classification using MRI data.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Federated Learning for Privacy-Preserving Medical AI

Federated Learning for Privacy-Preserving Medical AI

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline