Autorii : Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletarì Holger R. Roth Shadi Albarqouni Spyridon Bakas Mathieu N. Galtier Bennett A. Landman Klaus Maier-Hein Sébastien Ourselin Micah Sheller Ronald M. Summers Andrew Trask Dăncilă Xu Maximilian Baust M. Jorge Cardoso Authors: Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletarì Holger R. Roth Shadi Albarqouni Spyridon Bakas Mathieu N. Galtier în Votează Bennett A. Landman Klaus Maier-Hein Sébastien Ourselin Micah Sheller lui Ronald M. Summers Andrew Trask Dăncilă Xu Maximilian Baust M. Jorge Cardoso Abstract Învățarea automată bazată pe date (ML) a apărut ca o abordare promițătoare pentru construirea de modele statistice exacte și robuste din date medicale, care sunt colectate în volume uriașe de către sistemele moderne de asistență medicală. Datele medicale existente nu sunt exploatate pe deplin de ML în primul rând pentru că se află în silozuri de date și preocupările privind confidențialitatea restricționează accesul la aceste date. Cu toate acestea, fără acces la date suficiente, ML va fi împiedicat să-și atingă întregul potențial și, în cele din urmă, să facă tranziția de la cercetare la practica clinică. Acest document ia în considerare factorii cheie care contribuie la această problemă, explorează modul în care învățarea federată (FL) poate oferi o soluție pentru viitorul Introducere Research on artificial intelligence (AI), and particularly the advances in machine learning (ML) and deep learning (DL) have led to disruptive innovations in radiology, pathology, genomics and other fields. Modern DL models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data , , , . 1 2 3 4 5 For example, training an AI-based tumour detector requires a large database encompassing the full spectrum of possible anatomies, pathologies, and input data types. Data like this is hard to obtain, because health data is highly sensitive and its usage is tightly regulated . Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy De exemplu, este posibil să se reconstruiască fața unui pacient din datele tomografiei computerizate (CT) sau imagistica prin rezonanță magnetică (RMN). . Another reason why data sharing is not systematic in healthcare is that collecting, curating, and maintaining a high-quality data set takes considerable time, effort, and expense. Consequently such data sets may have significant business value, making it less likely that they will be freely shared. Instead, data collectors often retain fine-grained control over the data that they have gathered. 6 7 8 Federated learning (FL) , , is a learning paradigm seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself. Originally developed for different domains, such as mobile and edge device use cases , a câștigat recent tracțiune pentru aplicațiile de asistență medicală , , , , , , , FL permite obținerea de informații în colaborare, de exemplu, sub forma unui model de consens, fără a muta datele pacienților dincolo de firewall-urile instituțiilor în care locuiesc.În schimb, procesul ML are loc local la fiecare instituție participantă și numai caracteristicile modelului (de exemplu, parametrii, gradientele) sunt transferate așa cum este descris în Fig. Cercetările recente au arătat că modelele instruite de FL pot atinge niveluri de performanță comparabile cu cele instruite pe seturi de date găzduite central și superioare modelelor care văd doar date izolate de la o singură instituție. , . 9 10 11 12 13 14 15 16 17 18 19 20 1 16 17 FL aggregation server—the typical FL workflow in which a federation of training nodes receive the global model, resubmit their partially trained models to a central server intermittently for aggregation and then continue training on the consensus model that the server returns. FL peer-to-peer – formulare alternativă a FL în care fiecare nod de instruire își schimbă modelele parțial instruite cu unii sau toți colegii săi și fiecare face propria sa agregare. Centralised training—the general non-FL training workflow in which data acquiring sites donate their data to a central Data Lake from which they and others are able to extract data for local, independent training. a b c Implementarea cu succes a FL ar putea, prin urmare, să dețină un potențial semnificativ pentru a permite medicina de precizie la scară largă, ducând la modele care produc decizii imparțiale, reflectă în mod optim fiziologia unui individ și sunt sensibile la boli rare, respectând în același timp preocupările legate de guvernanță și confidențialitate. Avem în vedere un viitor federalizat pentru sănătatea digitală și cu acest document de perspectivă, împărtășim viziunea noastră de consens cu scopul de a oferi context și detalii comunității cu privire la beneficiile și impactul FL pentru aplicațiile medicale (secțiunea „Medicina bazată pe date necesită eforturi federalizate”), precum și evidențierea considerentelor cheie și provocărilor de implementare a FL pentru sănătatea digitală (secțiunea „considerente tehnice”). Medicina bazată pe date necesită eforturi federale ML și în special DL devine abordarea de facto a descoperirii cunoștințelor în multe industrii, dar implementarea cu succes a aplicațiilor bazate pe date necesită seturi de date mari și diverse. cu toate acestea, seturile de date medicale sunt dificil de obținut (subsecțiunea „Dependența de date”). FL abordează această problemă permițând învățarea colaborativă fără centralizarea datelor (subsecțiunea „Promisiunea eforturilor federalizate”) și și-a găsit deja calea către aplicațiile digitale de sănătate (subsecțiunea „Actualele eforturi FL pentru sănătatea digitală”). Dependența de date Data-driven approaches rely on data that truly represent the underlying data distribution of the problem. While this is a well-known requirement, state-of-the-art algorithms are usually evaluated on carefully curated data sets, often originating from only a few sources. This can introduce biases where demographics (e.g., gender, age) or technical imbalances (e.g., acquisition protocol, equipment manufacturer) skew predictions and adversely affect the accuracy for certain groups or sites. However, to capture subtle relationships between disease patterns, socio-economic and genetic factors, as well as complex and rare cases, it is crucial to expose a model to diverse cases. The need for large databases for AI training has spawned many initiatives seeking to pool data from multiple institutions. This data is often amassed into so-called Data Lakes. These have been built with the aim of leveraging either the commercial value of data, e.g., IBM’s Merge Healthcare acquisition , sau ca o resursă pentru creșterea economică și progresul științific, de exemplu, NHS Scoția National Safe Haven , French Health Data Hub , and Health Data Research UK . 21 22 23 24 Substantial, albeit smaller, initiatives include the Human Connectome , the UK Biobank , the Cancer Imaging Archive (TCIA) , NIH CXR8 , NIH DeepLesion , the Cancer Genome Atlas (TCGA) , the Alzheimer’s Disease Neuroimaging Initiative (ADNI) , as well as medical grand challenges such as the CAMELYON challenge , the International multimodal Brain Tumor Segmentation (BraTS) challenge , , or the Medical Segmentation Decathlon . Public medical data is usually task- or disease-specific and often released with varying degrees of license restrictions, sometimes limiting its exploitation. 25 26 27 28 29 30 31 32 33 34 35 36 37 Centralising or releasing data, however, poses not only regulatory, ethical and legal challenges, related to privacy and data protection, but also technical ones. Anonymising, controlling access and safely transferring healthcare data is a non-trivial, and sometimes impossible task. Anonymised data from the electronic health record can appear innocuous and GDPR/PHI compliant, but just a few data elements may allow for patient reidentification . The same applies to genomic data and medical images making them as unique as a fingerprint . Therefore, unless the anonymisation process destroys the fidelity of the data, likely rendering it useless, patient reidentification or information leakage cannot be ruled out. Gated access for approved users is often proposed as a putative solution to this issue. However, besides limiting data availability, this is only practical for cases in which the consent granted by the data owners is unconditional, since recalling data from those who may have had access to the data is practically unenforceable. 7 38 The promise of federated efforts The promise of FL is simple—to address privacy and data governance challenges by enabling ML from non-co-located data. In a FL setting, each data controller not only defines its own governance processes and associated privacy policies, but also controls data access and has the ability to revoke it. This includes both the training, as well as the validation phase. In this way, FL could create new opportunities, e.g., by allowing large-scale, in-institutional validation, or by enabling novel research on rare diseases, where the incident rates are low and data sets at each single institution are too small. Moving the model to the data and not vice versa has another major advantage: high-dimensional, storage-intense medical data does not have to be duplicated from local institutions in a centralised pool and duplicated again by every user that uses this data for local model training. As the model is transferred to the local institutions, it can scale naturally with a potentially growing global data set without disproportionately increasing data storage requirements. As depicted in Fig. , a FL workflow can be realised with different topologies and compute plans. The two most common ones for healthcare applications are via an aggregation server , , and peer to peer approaches , În toate cazurile, FL oferă implicit un anumit grad de confidențialitate, deoarece participanții la FL nu au niciodată acces direct la date de la alte instituții și primesc doar parametri de model care sunt agregați pe mai mulți participanți.Într-un flux de lucru FL cu server de agregare, instituțiile participante pot rămâne chiar necunoscute unul altuia. , , , Prin urmare, mecanisme precum confidențialitatea diferențială , sau învățarea din datele criptate a fost propusă pentru a spori în continuare confidențialitatea într-o setare FL (cf. secțiunea „Considerări tehnice”). and FL techniques are a growing area of research , . 2 16 17 18 15 39 40 41 42 43 44 45 46 12 20 FL topologies—communication architecture of a federation. Centralizat: serverul de agregare coordonează iterațiile de instruire și colectează, agregă și distribuie modelele către și de la nodurile de instruire (Hub & Spoke). Decentralised: each training node is connected to one or more peers and aggregation occurs on each node in parallel. Ierarhic: rețelele federate pot fi compuse din mai multe sub-federatii, care pot fi construite dintr-un amestec de federatii peer-to-peer și agregare server ( )). FL compute plans—trajectory of a model across several partners. Formarea secvențială/învățarea ciclică de transfer. Aggregation server, Peer to Peer. a b c d e f g Eforturile actuale ale FL pentru sănătatea digitală Since FL is a general learning paradigm that removes the data pooling requirement for AI model development, the application range of FL spans the whole of AI for healthcare. By providing an opportunity to capture larger data variability and to analyse patients across different demographics, FL may enable disruptive innovations for the future but is also being employed right now. În contextul înregistrărilor electronice de sănătate (EHR), de exemplu, FL ajută la reprezentarea și găsirea pacienților similari din punct de vedere clinic , , as well as predicting hospitalisations due to cardiac events , mortality and ICU stay time . The applicability and advantages of FL have also been demonstrated in the field of medical imaging, for whole-brain segmentation in MRI , precum și segmentarea tumorilor cerebrale , . Recently, the technique has been employed for fMRI classification to find reliable disease-related biomarkers and suggested as a promising approach in the context of COVID-19 . 13 47 14 19 15 16 17 18 48 It is worth noting that FL efforts require agreements to define the scope, aim and technologies used which, since it is still novel, can be difficult to pin down. In this context, today’s large-scale initiatives really are the pioneers of tomorrow’s standards for safe, fair and innovative collaboration in healthcare applications. These include consortia that aim to advance de cercetare, cum ar fi proiectul Trustworthy Federated Data Analytics (TFDA) and the German Cancer Consortium’s Joint Imaging Platform , which enable decentralised research across German medical imaging research institutions. Another example is an international research collaboration that uses FL for the development of AI models for the assessment of mammograms . The study showed that the FL-generated models outperformed those trained on a single institute’s data and were more generalisable, so that they still performed well on other institutes’ data. However, FL is not limited just to academic environments. academic 49 50 51 By linking healthcare institutions, not restricted to research centres, FL can have direct impact. The on-going HealthChain project , for example, aims to develop and deploy a FL framework across four hospitals in France. This solution generates common models that can predict treatment response for breast cancer and melanoma patients. It helps oncologists to determine the most effective treatment for each patient from their histology slides or dermoscopy images. Another large-scale effort is the Federated Tumour Segmentation (FeTS) initiative , care este o federație internațională de 30 de instituții de îngrijire a sănătății dedicate folosind un cadru FL open-source cu o interfață grafică de utilizator. Scopul este de a îmbunătăți detectarea limitelor tumorale, inclusiv gliomul cerebral, tumorile mamare, tumorile hepatice și leziunile osoase de la pacienții cu mielom multiplu. clinical 52 53 Another area of impact is within cercetare și traducere. FL permite cercetarea colaborativă chiar și pentru companii concurente. În acest context, una dintre cele mai mari inițiative este proiectul Melloddy . It is a project aiming to deploy multi-task FL across the data sets of 10 pharmaceutical companies. By training a common predictive model, which infers how chemical compounds bind to proteins, partners intend to optimise the drug discovery process without revealing their highly valuable in-house data. industrial 54 Impactul asupra părților interesate FL comprises a paradigm shift from centralised data lakes and it is important to understand its impact on the various stakeholders in a FL ecosystem. Clinicians Clinicians are usually exposed to a sub-group of the population based on their location and demographic environment, which may cause biased assumptions about the probability of certain diseases or their interconnection. By using ML-based systems, e.g., as a second reader, they can augment their own expertise with expert knowledge from other institutions, ensuring a consistency of diagnosis not attainable today. While this applies to ML-based system in general, systems trained in a federated fashion are potentially able to yield even less biased decisions and higher sensitivity to rare cases as they were likely exposed to a more complete data distribution. However, this demands some up-front effort such as compliance with agreements, e.g., regarding the data structure, annotation and report protocol, which is necessary to ensure that the information is presented to collaborators in a commonly understood format. Patients Patients are usually treated locally. Establishing FL on a global scale could ensure high quality of clinical decisions regardless of the treatment location. In particular, patients requiring medical attention in remote areas could benefit from the same high-quality ML-aided diagnoses that are available in hospitals with a large number of cases. The same holds true for rare, or geographically uncommon, diseases, that are likely to have milder consequences if faster and more accurate diagnoses can be made. FL may also lower the hurdle for becoming a data donor, since patients can be reassured that the data remains with their own institution and data access can be revoked. Hospitals and practices Hospitals and practices can remain in full control and possession of their patient data with complete traceability of data access, limiting the risk of misuse by third parties. However, this will require investment in on-premise computing infrastructure or private-cloud service provision and adherence to standardised and synoptic data formats so that ML models can be trained and evaluated seamlessly. The amount of necessary compute capability depends of course on whether a site is only participating in evaluation and testing efforts or also in training efforts. Even relatively small institutions can participate and they will still benefit from collective models generated. Cercetători și dezvoltatori Researchers and AI developers stand to benefit from access to a potentially vast collection of real-world data, which will particularly impact smaller research labs and start-ups. Thus, resources can be directed towards solving clinical needs and associated technical problems rather than relying on the limited supply of open data sets. At the same time, it will be necessary to conduct research on algorithmic strategies for federated training, e.g., how to combine models or updates efficiently, how to be robust to distribution shifts , , . FL-based development implies also that the researcher or AI developer cannot investigate or visualise all of the data on which the model is trained, e.g., it is not possible to look at an individual failure case to understand why the current model performs poorly on it. 11 12 20 Healthcare providers Furnizorii de servicii medicale din multe țări sunt afectați de schimbarea paradigmei în curs de desfășurare de la asistență medicală bazată pe volum, adică bazată pe costuri pentru servicii, la asistență medicală bazată pe valoare, care, la rândul său, este puternic legată de stabilirea cu succes a medicinei de precizie.Nu este vorba despre promovarea unor terapii individualizate mai scumpe, ci despre obținerea de rezultate mai bune mai devreme prin tratament mai concentrat, reducând astfel costurile. Manufacturers Manufacturers of healthcare software and hardware could benefit from FL as well, since combining the learning from many devices and applications, without revealing patient-specific information, can facilitate the continuous validation or improvement of their ML-based systems. However, realising such a capability may require significant upgrades to local compute, data storage, networking capabilities and associated software. Technical considerations FL is perhaps best-known from the work of Konečnỳ et al. , but various other definitions have been proposed in the literature , , , . A FL workflow (Fig. ) can be realised via different topologies and compute plans (Fig. ), but the goal remains the same, i.e., to combine knowledge learned from non-co-located data. In this section, we will discuss in more detail what FL is, as well as highlighting the key challenges and technical considerations that arise when applying FL in digital health. 55 9 11 12 20 1 2 Federated learning definition FL is a learning paradigm in which multiple parties train collaboratively without the need to exchange or centralise data sets. A general formulation of FL reads as follows: Let denote a global loss function obtained via a weighted combination of local losses , computed from private data care locuiește la părțile individuale implicate și nu este împărțită între ele: K Xk where > 0 denote the respective weight coefficients. wk În practică, fiecare participant obține și perfecționează de obicei un model de consens global, efectuând câteva runde de optimizare la nivel local și înainte de a partaja actualizări, fie direct, fie prin intermediul unui server de parametri. ) , . The actual process for aggregating parameters depends on the network topology, as nodes might be segregated into sub-networks due to geographical or legal constraints (see Fig. ). Aggregation strategies can rely on a single aggregating node (hub and spokes models), or on multiple nodes without any centralisation. An example is peer-to-peer FL, where connections exist between all or a subset of the participants and model updates are shared only between directly connected sites , , whereas an example of centralised FL aggregation is given in Algorithm 1. Note that aggregation strategies do not necessarily require information about the full model update; clients might chose to share only a subset of the model parameters for the sake of reducing communication overhead, ensure better privacy preservation or to produce multi-task learning algorithms having only part of their parameters learned in a federated manner. 1 9 12 2 15 56 10 A unifying framework enabling various training schemes may disentangle compute resources (data and servers) from the , as depicted in Fig. . The latter defines the trajectory of a model across several partners, to be trained and evaluated on specific data sets. compute plan 2 Provocări și considerații Despite the advantages of FL, it does not solve all issues that are inherent to learning on medical data. A successful model training still depends on factors like data quality, bias and standardisation . These issues have to be solved for both federated and non-federated learning efforts via appropriate measures, such as careful study design, common protocols for data acquisition, structured reporting and sophisticated methodologies for discovering bias and hidden stratification. In the following, we touch upon the key aspects of FL that are of particular relevance when applied to digital health and need to be taken into account when establishing FL. For technical details and in-depth discussion, we refer the reader to recent surveys , , . 2 11 12 20 Data heterogeneity Medical data is particularly diverse—not only because of the variety of modalities, dimensionality and characteristics in general, but even within a specific protocol due to factors such as acquisition differences, brand of the medical device or local demographics. FL may help address certain sources of bias through potentially increased diversity of data sources, but inhomogeneous data distribution poses a challenge for FL algorithms and strategies, as many are assuming independently and identically distributed (IID) data across the participants. In general, strategies such as are prone to fail under these conditions , , , în parte înfrângând chiar scopul strategiilor de învățare colaborativă. Rezultatele recente, totuși, indică faptul că formarea FL este încă fezabilă , even if medical data is not uniformly distributed across the institutions , or includes a local bias Cercetările care abordează această problemă includ, de exemplu, Strategia parțială de partajare a datelor and FL with domain-adaptation . Another challenge is that data heterogeneity may lead to a situation in which the global optimal solution may not be optimal for an individual local participant. The definition of model training optimality should, therefore, be agreed by all participants before training. FedAvg 9 9 57 58 59 16 17 51 FedProx 57 58 18 Privacy and security Healthcare data is highly sensitive and must be protected accordingly, following appropriate confidentiality procedures. Therefore, some of the key considerations are the trade-offs, strategies and remaining risks regarding the privacy-preserving potential of FL. Privacy vs. performance: It is important to note that FL does not solve all potential privacy issues and—similar to ML algorithms in general—will always carry some risks. Privacy-preserving techniques for FL offer levels of protection that exceed today’s current commercially available ML models . However, there is a trade-off in terms of performance and these techniques may affect, for example, the accuracy of the final model . Furthermore, future techniques and/or ancillary data could be used to compromise a model previously considered to be low-risk. 12 10 Level of trust: Broadly speaking, participating parties can enter two types of FL collaboration: —for FL consortia in which all parties are considered trustworthy and are bound by an enforceable collaboration agreement, we can eliminate many of the more nefarious motivations, such as deliberate attempts to extract sensitive information or to intentionally corrupt the model. This reduces the need for sophisticated counter-measures, falling back to the principles of standard collaborative research. Trusted —in FL systems that operate on larger scales, it might be impractical to establish an enforceable collaborative agreement. Some clients may deliberately try to degrade performance, bring the system down or extract information from other parties. Hence, security strategies will be required to mitigate these risks such as, advanced encryption of model submissions, secure authentication of all parties, traceability of actions, differential privacy, verification systems, execution integrity, model confidentiality and protections against adversarial attacks. Non-trusted Information leakage: By definition, FL systems avoid sharing healthcare data among participating institutions. However, the shared information may still indirectly expose private data used for local training, e.g., by model inversion of the model updates, the gradients themselves Atacuri adversare , . FL is different from traditional training insofar as the training process is exposed to multiple parties, thereby increasing the risk of leakage via reverse-engineering if adversaries can observe model changes over time, observe specific model updates (i.e., a single institution’s update), or manipulate the model (e.g., induce additional memorisation by others through gradient-ascent-style attacks). Developing counter-measures, such as limiting the granularity of the updates and adding noise , and ensuring adequate differential privacy , may be needed and is still an active area of research . 60 61 62 63 16 18 44 12 Traceability and accountability As per all safety-critical applications, the reproducibility of a system is important for FL in healthcare. In contrast to centralised training, FL requires multi-party computations in environments that exhibit considerable variety in terms of hardware, software and networks. Traceability of all system assets including data access history, training configurations, and hyperparameter tuning throughout the training processes is thus mandatory. In particular in non-trusted federations, traceability and accountability processes require execution integrity. After the training process reaches the mutually agreed model optimality criteria, it may also be helpful to measure the amount of contribution from each participant, such as computational resources consumed, quality of the data used for local training, etc. These measurements could then be used to determine relevant compensation, and establish a revenue model among the participants . One implication of FL is that researchers are not able to investigate data upon which models are being trained to make sense of unexpected results. Moreover, taking statistical measurements of their training data as part of the model development workflow will need to be approved by the collaborating parties as not violating privacy. Although each site will have access to its own raw data, federations may decide to provide some sort of secure intra-node viewing facility to cater for this need or may provide some other way to increase explainability and interpretability of the global model. 64 System architecture Spre deosebire de rularea FL pe scară largă printre dispozitivele de consum, cum ar fi McMahan et al. , healthcare institutional participants are equipped with relatively powerful computational resources and reliable, higher-throughput networks enabling training of larger models with many more local training steps, and sharing more model information between nodes. These unique characteristics of FL in healthcare also bring challenges such as ensuring data integrity when communicating by use of redundant nodes, designing secure encryption methods to prevent data leakage, or designing appropriate node schedulers to make best-use of the distributed computational devices and reduce idle time. 9 The administration of such a federation can be realised in different ways. In situations requiring the most stringent data privacy between parties, training may operate via some sort of “honest broker” system, in which a trusted third party acts as the intermediary and facilitates access to data. This setup requires an independent entity controlling the overall system, which may not always be desirable, since it could involve additional cost and procedural viscosity. However, it has the advantage that the precise internal mechanisms can be abstracted away from the clients, making the system more agile and simpler to update. In a peer-to-peer system each site interacts directly with some or all of the other participants. In other words, there is no gatekeeper function, all protocols must be agreed up-front, which requires significant agreement efforts, and changes must be made in a synchronised fashion by all parties to avoid problems. Additionally, in a trustless-based architecture the platform operator may be cryptographically locked into being honest by means of a secure protocol, but this may introduce significant computational overheads. Concluzie ML, and particularly DL, has led to a wide range of innovations in the area of digital healthcare. As all ML methods benefit greatly from the ability to access data that approximates the true global distribution, FL is a promising approach to obtain powerful, accurate, safe, robust and unbiased models. By enabling multiple parties to train collaboratively without the need to exchange or centralise data sets, FL neatly addresses issues related to egress of sensitive medical data. As a consequence, it may open novel research and business avenues and has the potential to improve patient care globally. However, already today, FL has an impact on nearly all stakeholders and the entire treatment cycle, ranging from improved medical image analysis providing clinicians with better diagnostic tools, over true precision medicine by helping to find similar patients, to collaborative and accelerated drug discovery decreasing cost and time-to-market for pharma companies. Not all technical questions have been answered yet and FL will certainly be an active research area throughout the next decade . 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International Conference on Machine Learning Acknowledgements This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), by the Wellcome Flagship Programme (WT213038/Z/18/Z), by the Intramural Research Programme of the National Institutes of Health (NIH) Clinical Center, by the National Cancer Institute of the NIH under award number U01CA242871, by the National Institute of Neurological Disorders and Stroke of the NIH under award number R01NS042645, as well as by the Helmholtz Initiative and Networking Fund (project “Trustworthy Federated Data Analytics”) and the PRIME programme of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF). The content and opinions expressed in this publication is solely the responsibility of the authors and do not necessarily represent those of the institutions they are affiliated with, e.g., the U.S. Department of Health and Human Services or the National Institutes of Health. Open access funding provided by Projekt DEAL. Această lucrare este disponibilă în natură sub licența CC by 4.0 Deed (Attribution 4.0 International). Această lucrare este disponibilă în natură sub licența CC by 4.0 Deed (Attribution 4.0 International).
