Bibliography

<table><tr><td>[Ashburner et al., 2005]</td><td>John Ashburner and Karl J Friston. "Unified segmentation". In: Neuroimage 26.3 (2005), pp. 839-851 (cit. on p. 32).</td></tr><tr><td>[Auei, 2002]</td><td>Peter Auer. "Using confidence bounds for exploitation-exploration trade-offs". In: Journal of Machine Learning Research 3.Nov (2002), pp. 397-422 (cit. on p. 55).</td></tr><tr><td>[Bagdasaryan et al., 2020]</td><td>Eugene Bagdasaryan et al. "How to backdoor federated learning". In: International Conference on Artificial Intelligence and Statistics. 2020, pp. 2938-2948 (cit. on p. 78).</td></tr><tr><td>[Bajaj et al., 2021]</td><td>Ishan Bajaj, Akhil Arora, and MM Faruque Hasan. "Black-box optimization: Methods and applications". In: Black box optimization, machine learning, and no-free lunch theorems. Springer, 2021, pp. 35-65 (cit. on p. 51).</td></tr><tr><td>[Bergstra et al., 2012]</td><td>James Bergstra and Yoshua Bengio. "Random search for hyper-parameter optimization." In: Journal of machine learning research 13.2 (2012) (cit. on pp. 51, 52).</td></tr><tr><td>[Bethlehem et al., 2022]</td><td>Richard AI Bethlehem et al. "Brain charts for the human lifespan". In: Nature 604.7906 (2022), pp. 525-533 (cit. on pp. 23, 41).</td></tr><tr><td>[Beutel et al., 2020]</td><td>Daniel J Beutel et al. "Flower: A friendly federated learning research framework". In: arXiv preprint arXiv:2007.14390 (2020) (cit. on p. 86).</td></tr><tr><td>[Bhagoji et al., 2019]</td><td>Arjun Nitin Bhagoji et al. "Analyzing federated learning through an adversarial lens". In: International Conference on Machine Learning. 2019, pp. 634-643 (cit. on pp. 78, 83).</td></tr><tr><td>[Bonawitz et al., 2017]</td><td>Keith Bonawitz et al. "Practical secure aggregation for privacy-preserving machine learning". In: proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017, pp. 1175-1191 (cit. on p. 17).</td></tr><tr><td>[Brisimi et al., 2018]</td><td>Theodora S Brisimi et al. "Federated learning of predictive models from federated electronic health records". In: International journal of medical informatics 112 (2018), pp. 59-67 (cit. on pp. 9, 15, 78).</td></tr></table>
<table>
  <tr>
    <td>[Brochu et al., 2010]</td>
    <td>Eric Brochu, Vlad M Cora, and Nando De Freitas. "A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning". In: arXiv preprint arXiv:1012.2599 (2010) (cit. on p. 52).</td>
  </tr>
  <tr>
    <td>[Bukaty, 2019]</td>
    <td>Preston Bukaty. The California Consumer Privacy Act (CCPA): An implementation guide. IT Governance Publishing, 2019. URL: http://www.jstor.org/stable/j.ctvjghvnn (visited on Oct. 31, 2022) (cit. on p. 22).</td>
  </tr>
  <tr>
    <td>[Bull, 2011]</td>
    <td>Adam D Bull. "Convergence rates of efficient global optimization algorithms." In: Journal of Machine Learning Research 12.10 (2011) (cit. on p. 50).</td>
  </tr>
  <tr>
    <td>[Calzada, 2022]</td>
    <td>Igor Calzada. "Citizens' data privacy in China: The state of the art of the Personal Information Protection Law (PIPL)". In: Smart Cities 5.3 (2022), pp. 1129-1150 (cit. on p. 22).</td>
  </tr>
  <tr>
    <td>[Chang et al., 2018]</td>
    <td>Ken Chang et al. "Distributed deep learning networks among institutions for medical imaging". In: Journal of the American Medical Informatics Association 25.8 (2018), pp. 945-954 (cit. on p. 15).</td>
  </tr>
  <tr>
    <td>[Chen et al., 2022]</td>
    <td>Andrew A Chen et al. "Privacy-preserving harmonization via distributed ComBat". In: Neuroimage 248 (2022), p. 118822 (cit. on pp. 23, 24, 27).</td>
  </tr>
  <tr>
    <td>[Cheung et al., 2023]</td>
    <td>Alexander TM Cheung et al. "Methods and impact for using federated learning to collaborate on clinical research". In: Neurosurgery 92.2 (2023), pp. 431-438 (cit. on p. 23).</td>
  </tr>
  <tr>
    <td>[Costafreda, 2009]</td>
    <td>Sergi G Costafreda. "Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies". In: Frontiers in neuroinformatics (2009), p. 33 (cit. on p. 9).</td>
  </tr>
  <tr>
    <td>[Dale et al., 1999]</td>
    <td>Anders M Dale, Bruce Fischl, and Martin I Sereno. "Cortical surface-based analysis: I. Segmentation and surface reconstruction". In: Neuroimage 9.2 (1999), pp. 179-194 (cit. on p. 32).</td>
  </tr>
  <tr>
    <td>[Dayan et al., 2021]</td>
    <td>Ittai Dayan et al. "Federated learning for predicting clinical outcomes in patients with COVID-19". In: Nature medicine 27.10 (2021), pp. 1735-1743 (cit. on p. 87).</td>
  </tr>
  <tr>
    <td>[Desikan et al., 2006]</td>
    <td>Rahul S Desikan et al. "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest". In: Neuroimage 31.3 (2006), pp. 968-980 (cit. on p. 32).</td>
  </tr>
  <tr>
    <td>[Di Martino et al., 2014]</td>
    <td>Adriana Di Martino et al. "The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism". In: Molecular psychiatry 19.6 (2014), pp. 659-667 (cit. on p. 32).</td>
  </tr>
</table>
<table>
   <tr>
    <td>[Dwork, 2006]</td>
    <td>Cynthia Dwork. "Differential privacy". In: Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33. Springer. 2006, pp. 1-12 (cit. on p. 10).</td>
   </tr>
   <tr>
    <td>[Eisenhauer, 2021]</td>
    <td>Joseph G Eisenhauer. "Meta-analysis and mega-analysis: A simple introduction". In: Teaching Statistics 43.1 (2021), pp. 21-27 (cit. on p. 11).</td>
   </tr>
   <tr>
    <td>[El Emam et al., 2014]</td>
    <td>Khaled El Emam and Bradley Malin. "Concepts and methods for de-identifying clinical trial data". In: Paper commissioned by the Committee on Strategies for Responsible Sharing of Clinical Trial Data (2014) (cit. on p. 9).</td>
   </tr>
   <tr>
    <td>[Ellis et al., 2009]</td>
    <td>Kathryn A Ellis et al. "The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease". In: International psychogeriatrics 21.4 (2009), pp. 672-687 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[European Commission, 2016]</td>
    <td>European Commission. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance). 2016. URL: https://eur-lex.europa.eu/eli/reg/2016/679/oj (cit. on p. 22).</td>
   </tr>
   <tr>
    <td>[Fischl, 2012]</td>
    <td>Bruce Fischl. "FreeSurfer". In: Neuroimage 62.2 (2012), pp. 774-781 (cit. on p. 90).</td>
   </tr>
   <tr>
    <td>[Fischl et al., 2004]</td>
    <td>Bruce Fischl et al. "Automatically parcellating the human cerebral cortex". In: Cerebral cortex 14.1 (2004), pp. 11-22 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Fortin et al., 2014]</td>
    <td>Jean-Philippe Fortin et al. "Functional normalization of 450k methylation array data improves replication in large cancer studies". In: Genome biology 15.11 (2014), pp. 1-17 (cit. on p. 67).</td>
   </tr>
   <tr>
    <td>[Fortin et al., 2017]</td>
    <td>Jean-Philippe Fortin et al. "Harmonization of multi-site diffusion tensor imaging data". In: Neuroimage 161 (2017), pp. 149-170 (cit. on p. 23).</td>
   </tr>
   <tr>
    <td>[Fortin et al., 2018]</td>
    <td>Jean-Philippe Fortin et al. "Harmonization of cortical thickness measurements across scanners and sites". In: Neuroimage 167 (2018), pp. 104-120 (cit. on pp. 23, 38).</td>
   </tr>
   <tr>
    <td>[Galtier et al., 2019]</td>
    <td>Mathieu N Galtier and Camille Marini. "Substra: a framework for privacy-preserving, traceable and collaborative machine learning". In: arXiv preprint arXiv:1910.11567 (2019) (cit. on pp. 83, 86).</td>
   </tr>
   <tr>
    <td>[Garcia-Dias et al., 2020]</td>
    <td>Rafael Garcia-Dias et al. "Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners". In: Neuroimage 220 (2020) (cit. on p. 66).</td>
   </tr>
  </table>
<table>
  <tr>
    <td>[Geyer et al., 2017]</td>
    <td>Robin C Geyer, Tassilo Klein, and Moin Nabi. "Differentially private federated learning: A client level perspective". In: arXiv preprint arXiv:1712.07557 (2017) (cit. on p. 17).</td>
  </tr>
  <tr>
    <td>[Glass, 1976]</td>
    <td>Gene V Glass. "Primary, secondary, and meta-analysis of research". In: Educational researcher 5.10 (1976), pp. 3–8 (cit. on p. 9).</td>
  </tr>
  <tr>
    <td>[Gupta et al., 2018]</td>
    <td>Otkrist Gupta and Ramesh Raskar. "Distributed learning of deep neural network over multiple agents". In: Journal of Network and Computer Applications 116 (2018), pp. 1–8 (cit. on p. 78).</td>
  </tr>
  <tr>
    <td>[He et al., 2020]</td>
    <td>Chaoyang He et al. "Fedml: A research library and benchmark for federated machine learning". In: arXiv preprint arXiv:2007.13518 (2020) (cit. on p. 83).</td>
  </tr>
  <tr>
    <td>[Hedges, 1992]</td>
    <td>Larry V Hedges. "Meta-analysis". In: Journal of Educational Statistics 17.4 (1992), pp. 279–296 (cit. on p. 10).</td>
  </tr>
  <tr>
    <td>[Higgins et al., 2019]</td>
    <td>Julian PT Higgins et al. Cochrane handbook for systematic reviews of interventions. John Wiley & Sons, 2019 (cit. on pp. 9, 10).</td>
  </tr>
  <tr>
    <td>[Hsu et al., 2019]</td>
    <td>Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. "Measuring the effects of non-identical data distribution for federated visual classification". In: arXiv preprint arXiv:1909.06335 (2019) (cit. on p. 30).</td>
  </tr>
  <tr>
    <td>[Jensen et al., 2012]</td>
    <td>Peter B Jensen, Lars J Jensen, and Søren Brunak. "Mining electronic health records: towards better research applications and clinical care". In: Nature Reviews Genetics 13.6 (2012), pp. 395–405 (cit. on p. 8).</td>
  </tr>
  <tr>
    <td>[Johnson et al., 2007]</td>
    <td>W Evan Johnson, Cheng Li, and Ariel Rabinovic. "Adjusting batch effects in microarray expression data using empirical Bayes methods". In: Biostatistics 8.1 (2007), pp. 118–127 (cit. on pp. 16, 23–26, 40, 65).</td>
  </tr>
  <tr>
    <td>[Jones et al., 1998a]</td>
    <td>Donald R Jones, Matthias Schonlau, and William J Welch. "Efficient Global Optimization of Expensive Black-Box Functions". In: Journal of Global Optimization 13.4 (1998), pp. 455–492. URL: https://doi.org/10.1023/A:1008306431147 (cit. on p. 54).</td>
  </tr>
  <tr>
    <td>[Jones et al., 1998b]</td>
    <td>Donald R Jones, Matthias Schonlau, and William J Welch. "Efficient global optimization of expensive black-box functions". In: Journal of Global optimization 13.4 (1998), p. 455 (cit. on p. 55).</td>
  </tr>
  <tr>
    <td>[Kesteren et al., 2019]</td>
    <td>Erik-Jan van Kesteren et al. "Privacy-preserving generalized linear models using distributed block coordinate descent". In: arXiv preprint arXiv:1911.03183 (2019) (cit. on pp. 23, 24).</td>
  </tr>
</table>
<table>
  <tr>
    <td>[Kim et al., 2017]</td>
    <td>Yejin Kim et al. "Federated tensor factorization for computational phenotyping". In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017, pp. 887-895 (cit. on p. 78).</td>
  </tr>
  <tr>
    <td>[Kim et al., 2022]</td>
    <td>SeungWook Kim et al. "Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model". In: Frontiers in Aging Neuroscience 14 (2022) (cit. on p. 65).</td>
  </tr>
  <tr>
    <td>[Kingma et al., 2014]</td>
    <td>Diederik P Kingma and Jimmy Ba. "Adam: A method for stochastic optimization". In: arXiv preprint arXiv:1412.6980 (2014) (cit. on p. 15).</td>
  </tr>
  <tr>
    <td>[Konečný et al., 2016]</td>
    <td>Jakub Konečný et al. "Federated learning: Strategies for improving communication efficiency". In: arXiv preprint arXiv:1610.05492 (2016) (cit. on pp. 13, 15, 23, 78, 79).</td>
  </tr>
  <tr>
    <td>[LeCun et al., 2010]</td>
    <td>Yann LeCun and Corinna Cortes. "MNIST handwritten digit database". In: (2010). URL: http://yann.lecun.com/exdb/mnist/ (cit. on p. 88).</td>
  </tr>
  <tr>
    <td>[Lee et al., 2018]</td>
    <td>Junghye Lee et al. "Privacy-preserving patient similarity learning in a federated environment: development and analysis". In: JMIR medical informatics 6.2 (2018), e20 (cit. on p. 78).</td>
  </tr>
  <tr>
    <td>[Leek et al., 2012]</td>
    <td>Jeffrey T Leek et al. "The sva package for removing batch effects and other unwanted variation in high-throughput experiments". In: Bioinformatics 28.6 (2012), pp. 882-883 (cit. on p. 65).</td>
  </tr>
  <tr>
    <td>[Li et al., 2018]</td>
    <td>Tian Li et al. "Federated optimization in heterogeneous networks". In: arXiv preprint arXiv:1812.06127 (2018) (cit. on pp. 17, 50, 78).</td>
  </tr>
  <tr>
    <td>[Li et al., 2019a]</td>
    <td>Tian Li et al. "Feddane: A federated newton-type method". In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE. 2019, pp. 1227-1231 (cit. on pp. 50, 78).</td>
  </tr>
  <tr>
    <td>[Li et al., 2019b]</td>
    <td>Wenqi Li et al. "Privacy-preserving federated brain tumour segmentation". In: International workshop on machine learning in medical imaging. Springer. 2019, pp. 133-141 (cit. on p. 23).</td>
  </tr>
  <tr>
    <td>[Li et al., 2019c]</td>
    <td>Xiang Li et al. "On the convergence of fedavg on non-iid data". In: arXiv preprint arXiv:1907.02189 (2019) (cit. on pp. 15, 27).</td>
  </tr>
  <tr>
    <td>[Li et al., 2020a]</td>
    <td>Tian Li et al. "Federated learning: Challenges, methods, and future directions". In: IEEE Signal Processing Magazine 37.3 (2020), pp. 50-60 (cit. on pp. 15, 16, 40, 66, 78).</td>
  </tr>
  <tr>
    <td>[Li et al., 2020b]</td>
    <td>Xiaoxiao Li et al. "Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results". In: Medical Image Analysis 65 (2020), p. 101765 (cit. on p. 23).</td>
  </tr>
  <tr>
    <td>[Lim et al., 2020]</td>
    <td>Wei Yang Bryan Lim et al. "Federated learning in mobile edge networks: A comprehensive survey". In: IEEE Communications Surveys & Tutorials 22.3 (2020), pp. 2031-2063 (cit. on p. 16).</td>
  </tr>
</table>
<table>
   <tr>
    <td>[Liu et al., 2021]</td>
    <td>Yang Liu et al. "Fate: An industrial grade platform for collaborative learning with data protection". In: <i>The Journal of Machine Learning Research</i> 22.1 (2021), pp. 10320-10325 (cit. on pp. 83, 86).</td>
   </tr>
   <tr>
    <td>[Lorenzi et al., 2018]</td>
    <td>Marco Lorenzi et al. "Susceptibility of brain atrophy to TRIB3 in Alzheimer's disease, evidence from functional prioritization in imaging genetics". In: <i>Proceedings of the National Academy of Sciences</i> 115.12 (2018), pp. 3162-3167 (cit. on p. 29).</td>
   </tr>
   <tr>
    <td>[Ludwig et al., 2020]</td>
    <td>Heiko Ludwig et al. "Ibm federated learning: an enterprise framework white paper v0. 1". In: <i>arXiv preprint arXiv:2007.10987</i> (2020) (cit. on p. 83).</td>
   </tr>
   <tr>
    <td>[Lydia et al., 2019]</td>
    <td>Agnes Lydia and Sagayaraj Francis. "Adagrad—an optimizer for stochastic gradient descent". In: <i>Int. J. Inf. Comput. Sci</i> 6.5 (2019), pp. 566-568 (cit. on p. 15).</td>
   </tr>
   <tr>
    <td>[Ma et al., 2019]</td>
    <td>Yanjun Ma et al. "PaddlePaddle: An open-source deep learning platform from industrial practice". In: <i>Frontiers of Data and Domputing</i> 1.1 (2019), pp. 105-115 (cit. on pp. 83, 86).</td>
   </tr>
   <tr>
    <td>[MacKay et al., 2003]</td>
    <td>David JC MacKay and David JC Mac Kay. <i>Information theory, inference and learning algorithms</i>. Cambridge university press, 2003 (cit. on p. 52).</td>
   </tr>
   <tr>
    <td>[Mahlool et al., 2022]</td>
    <td>Dhurgham Hassan Mahlool and Mohamed Hamzah Abed. "Distributed brain tumor diagnosis using a federated learning environment". In: <i>Bulletin of Electrical Engineering and Informatics</i> 11.6 (2022). Cited by: 0; All Open Access, Gold Open Access, Green Open Access, pp. 3313-3321. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139133360&doi=10.11591/2feei.v11i6.4131&amp;partnerID=40&amp;md5=1ad3123e9db42a98e13e51bd26fdcbc1 (cit. on p. 23).</td>
   </tr>
   <tr>
    <td>[Marcus et al., 2010]</td>
    <td>Daniel S Marcus et al. "Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults". In: <i>Journal of cognitive neuroscience</i> 22.12 (2010), pp. 2677-2684 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Marek et al., 2018]</td>
    <td>Kenneth Marek et al. "The Parkinson's progression markers initiative (PPMI)—establishing a PD biomarker cohort". In: <i>Annals of clinical and translational neurology</i> 5.12 (2018), pp. 1460-1477 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Mathew et al., 1999]</td>
    <td>Thomas Mathew and Kenneth Nordstrom. "On the equivalence of meta-analysis using literature and using individual patient data". In: <i>Biometrics</i> 55.4 (1999), pp. 1221-1223 (cit. on p. 11).</td>
   </tr>
   <tr>
    <td>[McMahan et al., 2017]</td>
    <td>Brendan McMahan et al. "Communication-efficient learning of deep networks from decentralized data". In: <i>Artificial intelligence and statistics</i>. PMLR. 2017, pp. 1273-1282 (cit. on pp. 13, 14, 23, 27, 50, 57, 79).</td>
   </tr>
  </table>
<table>
   <tr>
    <td>[Merkel, 2014]</td>
    <td>Dirk Merkel. “Docker: lightweight linux containers for consistent development and deployment”. In: Linux journal 2014.239 (2014), p. 2 (cit. on p. 82).</td>
   </tr>
   <tr>
    <td>[Miotto et al., 2018]</td>
    <td>Riccardo Miotto et al. “Deep learning for healthcare: review, opportunities and challenges”. In: Briefings in bioinformatics 19.6 (2018), pp. 1236–1246 (cit. on p. 7).</td>
   </tr>
   <tr>
    <td>[Mockus, 1998]</td>
    <td>Jonas Mockus. “The application of Bayesian methods for seeking the extremum”. In: Towards global optimization 2 (1998), p. 117 (cit. on p. 54).</td>
   </tr>
   <tr>
    <td>[Murdoch et al., 2013]</td>
    <td>Travis B Murdoch and Allan S Detsky. “The inevitable application of big data to health care”. In: Jama 309.13 (2013), pp. 1351–1352 (cit. on p. 7).</td>
   </tr>
   <tr>
    <td>[Orlhac et al., 2022]</td>
    <td>Fanny Orlhac et al. “A guide to ComBat harmonization of imaging biomarkers in multicenter studies”. In: Journal of Nuclear Medicine 63.2 (2022), pp. 172–179 (cit. on pp. 16, 65).</td>
   </tr>
   <tr>
    <td>[Pardalos et al., 2021]</td>
    <td>Panos M Pardalos, Varvara Rasskazova, Michael N Vrahatis, et al. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer, 2021 (cit. on p. 58).</td>
   </tr>
   <tr>
    <td>[Paszke et al., 2019]</td>
    <td>Adam Paszke et al. “Pytorch: An imperative style, high-performance deep learning library”. In: Advances in neural information processing systems. 2019, pp. 8026–8037 (cit. on p. 88).</td>
   </tr>
   <tr>
    <td>[Perrone et al., 2019]</td>
    <td>Valerio Perrone et al. Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. Tech. rep. 2019 (cit. on pp. 57, 59).</td>
   </tr>
   <tr>
    <td>[Plis et al., 2016]</td>
    <td>Sergey M Plis et al. “COINSTAC: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data”. In: Frontiers in neuroscience 10 (2016), p. 365 (cit. on p. 79).</td>
   </tr>
   <tr>
    <td>[Pomponio et al., 2020]</td>
    <td>Raymond Pomponio et al. “Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan”. In: NeuroImage 208 (2020), p. 116450 (cit. on pp. 16, 23–26, 29, 38, 43, 44).</td>
   </tr>
   <tr>
    <td>[Real et al., 2019]</td>
    <td>Esteban Real et al. “Regularized evolution for image classifier architecture search”. In: Proceedings of the aaai conference on artificial intelligence. Vol. 33. 01. 2019, pp. 4780–4789 (cit. on p. 51).</td>
   </tr>
   <tr>
    <td>[Reddi et al., 2020]</td>
    <td>Sashank Reddi et al. “Adaptive federated optimization”. In: arXiv preprint arXiv:2003.00295 (2020) (cit. on p. 14).</td>
   </tr>
   <tr>
    <td>[Reina et al., 2021]</td>
    <td>G Anthony Reina et al. “OpenFL: An open-source framework for Federated Learning”. In: arXiv preprint arXiv:2105.06413 (2021) (cit. on pp. 83, 87).</td>
   </tr>
   <tr>
    <td>[Reynolds et al., 2022]</td>
    <td>Maxwell Reynolds et al. “Combat harmonization: Empirical bayes versus fully bayes approaches”. In: bioRxiv (2022) (cit. on pp. 30, 33).</td>
   </tr>
  </table>
<table>
   <tr>
    <td>[Rieke et al., 2020]</td>
    <td>Nicola Rieke et al. "The future of digital health with federated learning". In: NPJ digital medicine 3.1 (2020), pp. 1–7 (cit. on p. 50).</td>
   </tr>
   <tr>
    <td>[Rosenthal et al., 2016]</td>
    <td>Liana S Rosenthal et al. "The NINDS Parkinson's disease biomarkers program". In: Movement Disorders 31.6 (2016), pp. 915–923 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Roth et al., 2020]</td>
    <td>Holger R Roth et al. "Federated learning for breast density classification: A real-world implementation". In: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 2. Springer. 2020, pp. 181–191 (cit. on p. 87).</td>
   </tr>
   <tr>
    <td>[Roth et al., 2022]</td>
    <td>Holger R Roth et al. "NVIDIA FLARE: Federated Learning from Simulation to Real-World". In: arXiv preprint arXiv:2210.13291 (2022) (cit. on p. 83).</td>
   </tr>
   <tr>
    <td>[Rumsfeld et al., 2016]</td>
    <td>John S Rumsfeld, Karen E Joynt, and Thomas M Maddox. "Big data analytics to improve cardiovascular care: promise and challenges". In: Nature Reviews Cardiology 13.6 (2016), pp. 350–359 (cit. on p. 8).</td>
   </tr>
   <tr>
    <td>[Ryffel et al., 2018]</td>
    <td>Theo Ryffel et al. "A generic framework for privacy preserving deep learning". In: arXiv preprint arXiv:1811.04017 (2018) (cit. on p. 80).</td>
   </tr>
   <tr>
    <td>[Sarma et al., 2021]</td>
    <td>Karthik V Sarma et al. "Federated learning improves site performance in multicenter deep learning without data sharing". In: Journal of the American Medical Informatics Association 28.6 (2021), pp. 1259–1264 (cit. on p. 87).</td>
   </tr>
   <tr>
    <td>[Satizabal et al., 2019]</td>
    <td>Claudia L Satizabal et al. "Genetic architecture of subcortical brain structures in 38,851 individuals". In: Nature genetics 51.11 (2019), pp. 1624–1636 (cit. on p. 11).</td>
   </tr>
   <tr>
    <td>[Sattler et al., 2019]</td>
    <td>Felix Sattler et al. "Robust and communication-efficient federated learning from non-iid data". In: IEEE transactions on neural networks and learning systems 31.9 (2019), pp. 3400–3413 (cit. on p. 17).</td>
   </tr>
   <tr>
    <td>[Schmaal et al., 2016]</td>
    <td>Lianne Schmaal et al. "Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group". In: Molecular psychiatry 21.6 (2016), pp. 806–812 (cit. on p. 11).</td>
   </tr>
   <tr>
    <td>[Schneeweiss, 2014]</td>
    <td>Sebastian Schneeweiss. "Learning from big health care data". In: N Engl J Med 370.23 (2014), pp. 2161–2163 (cit. on p. 7).</td>
   </tr>
  </table>
<table>
   <tr>
    <td>[Sheller et al., 2019]</td>
    <td>Micah J Sheller et al. "Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation". In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4. Springer. 2019, pp. 92–104 (cit. on p. 15).</td>
   </tr>
   <tr>
    <td>[Shen et al., 2016]</td>
    <td>Shiqi Shen, Shruti Tople, and Prateek Saxena. "Auror: Defending against poisoning attacks in collaborative deep learning systems". In: Proceedings of the 32nd Annual Conference on Computer Security Applications. 2016, pp. 508–519 (cit. on p. 83).</td>
   </tr>
   <tr>
    <td>[Silva et al., 2019]</td>
    <td>Santiago Silva et al. "Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data". In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE. 2019, pp. 270–274 (cit. on pp. 27, 29, 80, 90).</td>
   </tr>
   <tr>
    <td>[Sled et al., 1998]</td>
    <td>John G Sled, Alex P Zijdenbos, and Alan C Evans. "A nonparametric method for automatic correction of intensity nonuniformity in MRI data". In: IEEE transactions on medical imaging 17.1 (1998), pp. 87–97 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Smith et al., 2017]</td>
    <td>Virginia Smith et al. "Federated multi-task learning". In: Advances in neural information processing systems 30 (2017) (cit. on p. 16).</td>
   </tr>
   <tr>
    <td>[Solove et al., 2020]</td>
    <td>Daniel J Solove and Paul M Schwartz. Information privacy law. Aspen Publishing, 2020 (cit. on pp. 8, 9, 11).</td>
   </tr>
   <tr>
    <td>[Sperling et al., 2014]</td>
    <td>Reisa A Sperling et al. "The A4 study: stopping AD before symptoms begin?" In: Science translational medicine 6.228 (2014), 228fs13–228fs13 (cit. on p. 32).</td>
   </tr>
   <tr>
    <td>[Sun et al., 2013]</td>
    <td>Jimeng Sun and Chandan K Reddy. "Big data analytics for healthcare". In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013, pp. 1525–1525 (cit. on p. 8).</td>
   </tr>
   <tr>
    <td>[Thompson et al., 2014]</td>
    <td>Paul M Thompson et al. "The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data". In: Brain imaging and behavior 8.2 (2014), pp. 153–182 (cit. on p. 93).</td>
   </tr>
   <tr>
    <td>[Torous et al., 2018]</td>
    <td>John Torous et al. "Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements". In: BMJ Ment Health 21.3 (2018), pp. 116–119 (cit. on p. 7).</td>
   </tr>
   <tr>
    <td>[Undavia et al., 2020]</td>
    <td>Jaimin Navinchandra Undavia and Atul Manubhai Patel. "Big Data Analytics in Healthcare: Applications and Challenges". In: International Journal of Big Data and Analytics in Healthcare (IJBDAH) 5.1 (2020), pp. 19–27 (cit. on p. 7).</td>
   </tr>
  </table>
<table>
  <tr>
    <td>[Voigt et al., 2017]</td>
    <td>Paul Voigt and Axel Von dem Bussche. “The eu general data protection regulation (gdpr)”. In: A Practical Guide, 1st Ed., Cham: Springer International Publishing (2017) (cit. on pp. 8, 11, 78).</td>
  </tr>
  <tr>
    <td>[Wachinger et al., 2021]</td>
    <td>Christian Wachinger et al. “Detect and correct bias in multi-site neuroimaging datasets”. In: Medical Image Analysis 67 (2021), p. 101879 (cit. on pp. 16, 22).</td>
  </tr>
  <tr>
    <td>[Webb-Vargas et al., 2017]</td>
    <td>Yenny Webb-Vargas et al. “Big data and neuroimaging”. In: Statistics in biosciences 9 (2017), pp. 543–558 (cit. on pp. 8, 9).</td>
  </tr>
  <tr>
    <td>[Weiner et al., 2013]</td>
    <td>Michael W Weiner et al. “The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception”. In: Alzheimer’s &amp; Dementia 9.5 (2013), e111–e194 (cit. on p. 32).</td>
  </tr>
  <tr>
    <td>[Wosik et al., 2020]</td>
    <td>Jedrek Wosik et al. “Telehealth transformation: COVID-19 and the rise of virtual care”. In: Journal of the American Medical Informatics Association 27.6 (2020), pp. 957–962 (cit. on p. 7).</td>
  </tr>
  <tr>
    <td>[Yang et al., 2019]</td>
    <td>Qiang Yang et al. “Federated machine learning: Concept and applications”. In: ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019), pp. 1–19 (cit. on p. 78).</td>
  </tr>
  <tr>
    <td>[Yuhong et al., 2019]</td>
    <td>Wen Yuhong et al. Federated Learning powered by NVIDIA Clara. 2019. URL: https://devblogs.nvidia.com/federated-learning-clara/ (visited on June 22, 2020) (cit. on p. 79).</td>
  </tr>
  <tr>
    <td>[Zaheer et al., 2018]</td>
    <td>Manzil Zaheer et al. “Adaptive methods for nonconvex optimization”. In: Advances in neural information processing systems 31 (2018) (cit. on p. 15).</td>
  </tr>
  <tr>
    <td>[Zhao et al., 2018]</td>
    <td>Yue Zhao et al. “Federated learning with non-iid data”. In: arXiv preprint arXiv:1806.00582 (2018) (cit. on p. 16).</td>
  </tr>
  <tr>
    <td>[Ziller et al., 2021]</td>
    <td>Alexander Ziller et al. “Pysyft: A library for easy federated learning”. In: Federated Learning Systems: Towards Next-Generation AI (2021), pp. 111–139 (cit. on pp. 83, 84).</td>
  </tr>
  <tr>
    <td>[Zugman et al., 2022]</td>
    <td>André Zugman et al. “Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group”. In: Human Brain Mapping 43.1 (2022), pp. 255–277 (cit. on p. 11).</td>
  </tr>
  <tr>
    <td>[Zuo et al., 2021]</td>
    <td>Lianrui Zuo et al. “Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory”. In: NeuroImage 243 (2021), p. 118569 (cit. on p. 66).</td>
  </tr>
</table>