{"doi":"10.1016/j.xops.2025.101030","title":"Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography","abstract":"Purpose: To conduct a comprehensive systematic evaluation of federated learning (FL) strategies for multi-disease retinal classification using OCT angiography (OCTA), implementing a 2-part experimental framework to establish foundational feasibility and optimize performance under realistic heterogeneous conditions while ensuring privacy preservation. Design: = 0.5). Participants: A total of 456 OCTA images from patients with 7 retinal pathologies, with diabetic retinopathy (31.1%) and normal cases (25.2%) comprising the majority, sourced from the public OCTA-500 data set (n = 300) and a private collection from the University of Illinois Chicago (n = 156). Methods: Five FL aggregation strategies (federated averaging [FedAvg], federated proximal [FedProx], federated magnetic resonance imaging [FedMRI], federated Adagrad, and federated Yogi) were systematically evaluated across multiple optimization dimensions: 7 architecture configurations spanning vision transformers, established convolutional neural networks, and hybrid models; 5 transfer learning freezing strategies; 3 local epoch configurations (2, 5, and 10); and scalability analysis across 2, 3, and 5-client federations. Security mechanisms including differential privacy (ε = 1.0-8.0) and secure aggregation were integrated and evaluated. Performance was assessed across 3 classification scenarios: 7-class, 4-class modified, and 4-class streamlined. Main Outcome Measures: Classification accuracy, receiver-operating-characteristic area under the curve (ROC-AUC), and macro-averaged F1-score with comprehensive privacy-utility analysis and computational efficiency metrics. Results: Under controlled conditions, FL achieved superior performance in simplified classifications, with FedAvg, FedProx, and FedMRI reaching 72.09% accuracy versus 69.77% centralized training. Comprehensive optimization identified DenseNet121 as optimal architecture (79.55% accuracy, 89.68% ROC-AUC), with \"most\" freezing strategy (75% frozen layers) providing 60% training time reduction while maintaining superior performance. Federated proximal demonstrated exceptional resilience to heterogeneity (-11.7% degradation). Bonawitz secure aggregation achieved optimal privacy-utility balance (63.64% accuracy with cryptographic guarantees), whereas differential privacy maintained clinical utility under moderate constraints (ε ≈ 4-6). Conclusions: This systematic evaluation establishes FL as a comprehensive solution for privacy-preserving multi-institutional OCTA-based disease classification, with careful architectural selection, optimization strategies, and security mechanisms enabling performance that matches or exceeds centralized approaches while maintaining regulatory compliance and clinical utility. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.","journal":"Ophthalmology Science","year":2025,"id":535854,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":29.844511487584125,"corpus_rank":8690,"citation_count":2,"citer_count":2,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":12.5,"fair_percentile":28.767960868236013,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1379984,"name":"Sina Gholami","orcid":"0009-0007-5488-4998","position":1,"is_corresponding":false},{"id":304777,"name":"Theodore Leng","orcid":"0000-0002-8461-3562","position":2,"is_corresponding":false},{"id":845601,"name":"Jennifer I. Lim","orcid":"0000-0001-7791-6998","position":3,"is_corresponding":false},{"id":880532,"name":"Minhaj Nur Alam","orcid":"0000-0003-3095-2232","position":4,"is_corresponding":false},{"id":1420063,"name":"Ahammed Sakir Nabil","orcid":"0009-0005-1066-6062","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T02:52:00.885532Z","pmid":"41732778","pmcid":"PMC12925154","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":33.3333,"fair_a":6.25,"fair_i":20.0,"fair_r":25.0,"fair_zscore":-0.8688,"fair_rationale":{"fair_score":12.5,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":33.33,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier string (DOI, Handle, ARK, or repository accession) is assigned to this study's combined dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"OCTA-500 data set is publicly available and private University of Illinois Chicago (UIC) data can be shared upon request and approval of the corresponding author.","grounded":true,"rationale":"The study's own combined dataset is not deposited in a named repository; the private portion is held by the authors.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"OCTA-500 data set is publicly available and private University of Illinois Chicago (UIC) data can be shared upon request and approval of the corresponding author.","grounded":true,"rationale":"The statement points to a person for the private data, not a repository record, placing it in Colavizza category 1.","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"Table 1 Training, Validation, and Test Set Distribution across 7 Retinal Disease Classes for Scenario A, Showing Sample Counts from OCTA-500 and UIC Data Sets with Stratified Splitting Maintaining 80%-10%-10% Ratio","grounded":true,"rationale":"The paper includes itemised tables (Tables 1–3) that enumerate the number of images per class and source, constituting an inventory of the dataset. 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A reader — and a harvester — should not have to infer the access level from the presence of a download link.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No access-level label is applied to the data. [majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"i_community_standard_vocabulary","dimension":"I","label":"Community standard / vocabulary","action":"Adopt and NAME your domain's data standard — the minimum-information checklist, metadata schema, or ontology your community uses (MIAME/MINSEQE, ISA-Tab, BIDS, an OBO ontology, HL7 FHIR/OMOP) — and say which one you followed. A reporting checklist standardises your paper; it does nothing for your data. 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[majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"r_documentation_codebook","dimension":"R","label":"Documentation / codebook","action":"Ship a README and a data dictionary IN the deposit — every file, every variable, its units, its allowed values, its missing-value codes. It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"Table 1. Training, Validation, and Test Set Distribution across 7 Retinal Disease Classes for Scenario A, ...","why":"The dataset's class definitions are provided inside the article in tables, not as a separate documentation object. 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Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:29:55.331569Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}