የቅጂ መብት: Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletarì Holger R. Roth አግኝቷል ፎቶዎች ፎቶዎች Mathieu N. Galtier የ Bennett A. Landman ግምገማ ኬሚካል ኬሚካል Sébastien Ourselin Micah Sheller Ronald M. Summers ፎቶ ፎቶ Daguang Xu Maximilian Baust ጂ. Jorge Cardoso የቅጂ መብት: Nicola Rieke Jonny Hancox Wenqi Li ፌስቡክ Millet Holger R. Roth አግኝቷል ፎቶዎች ፎቶዎች Mathieu N. Galtier የ Bennett A. Landman ግምገማ ኬሚካል ኬሚካል ፎቶዎች ፎቶዎች Micah Sheller Ronald M. Summers ፎቶ ፎቶ አግኝቷል Maximilian Baust ጂ. Jorge Cardoso አጠቃቀም የ Data-driven machine learning (ML) የዲጂታል የሕክምና ስርዓቶች በከፍተኛ መጠን ውስጥ አጠቃቀም የሚሆን የሕክምና ውሂብ ከ ትክክክለኛ እና ጠንካራ ስቴትስቲክ ሞዴሎች ለመፍጠር አንድ አስቸጋሪ መተግበሪያ ነው. የአሁኑ የሕክምና ውሂብ በዲጂታል silos እና የግል ደህንነት ደንበኞች ይህን ውሂብ መዳረሻ ለመጠበቅ ምክንያት በዲጂታል የሕክምና ስርዓቶች በከፍተኛ መጠን ጥቅም ላይ ጥቅም ላይ አይችልም. ይሁን እንጂ በዲጂታል ሕክምና መኖሪያ ቤት (FL) የዲጂታል ሕክምና ፍላጎቶች እና ፍላጎቶች ለማስተካከል የሚፈልጉትን ፍላጎቶች እና ግምገማዎች ይሰጣሉ. አግኙን የቴክኒክ ኢንተርኔት (AI) ምርምር, እና በተለይም ማሽን መፍጠር (ML) እና ጥልቅ መፍጠር (DL) ውስጥ ልማት በአሁኑ ጊዜ የ DL ሞዴሎች በኬሚካል ደረጃ ላይ ትክክለኛነት ለማግኘት በክሊኒካል ደረጃ ትክክለኛነት ለማግኘት በከፍተኛ መጠን የተቀየደው ውሂብ ስብስቦች ከ ማወቅ ያስፈልጋቸዋል, እንዲሁም ደህንነት, ትክክለኛነት, ትክክክለኛነት እና በከፍተኛ መጠን ላይ ውሂብ ለማሟላት ይፈልጋቸዋል. , , , . 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 ለምሳሌ, በኮምፒውተር ቶሞግራፊያ (ኮምፒውተር ቶሞግራፊያ) ወይም የ Magnetic Resonance Imaging (MRI) ውሂብ ላይ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክብደት ውስጥ በክ አንድ ሌሎች ምክንያት የግል መረጃ ልውውጥ በጤና ውስጥ ስርዓት አይሆንም ይህም ከፍተኛ ጥራት የግል መረጃ ስብስቦች ለማከናወን, ማከናወን እና ለማከናወን ብዙ ጊዜ, ደንበኞች እና ወጪዎች ይወስዳል. ስለዚህ እንደዚህ የግል መረጃ ስብስቦች በጣም አስፈላጊ የንግድ ዋጋ ሊሆን ይችላል, እነርሱ በነጻ ማከናወን ይቻላል. 6 7 8 Federated learning (FL) , , የ Data Governance እና የግል ደህንነት ችግሮችን ለማስተካከል በመተግበሪያ አግኝቷል አንድ መግቢያ ፓርዳሚም በይነገጽ አግኝቷል, በይነገጽ አግኝቷል እና በይነገጽ አግኝቷል. በአሁኑ ጊዜ, የጤና መተግበሪያዎች ላይ ትኩረት ያገኛል. , , , , , , , በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) እና በኤሌክትሮኒክስ (ኤሌክትሮኒክስ) ውስጥ በኤሌክትሮኒ የቅርብ ጊዜ ምርምር በ FL የተቀየደው ሞዴሎች በ centrally hosted datasets ላይ የተቀየደው ሞዴሎች ጋር ተመሳሳይ አፈጻጸም ደረጃዎችን ማግኘት ይችላሉ እና ብቻ ከባድ አንድ ተቋማት ውሂብ ለማየት ሞዴሎች በላይ ሊሆን ይችላል. , . 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—እያንዳንዱ training node ከሌሎች ወይም ከሌሎች ከሌሎች ጋር ከሌሎች የተቀየደው ሞዴሎች ይቀየዳል እና እያንዳንዱ የግል አጠቃቀም ያደርጋል. የ centralized training—አጠቃላይ የ non-FL training workflow በ Data Acquiring Sites ውስጥ የእርስዎን ውሂብ ወደ አንድ ማዕከል Data Lake ይሰጣሉ, ይህም እነርሱ እና ሌሎች ሰዎች በአካባቢው, የግል ስልጠና ለማግኘት ውሂብ ማሸግ ይችላሉ. a b c A successful implementation of FL could thus hold a significant potential for enabling precision medicine at large-scale, leading to models that yield unbiased decisions, optimally reflect an individual’s physiology, and are sensitive to rare diseases while respecting governance and privacy concerns. However, FL still requires rigorous technical consideration to ensure that the algorithm is proceeding optimally without compromising safety or patient privacy. Nevertheless, it has the potential to overcome the limitations of approaches that require a single pool of centralised data. እኛ የዲጂታል ሕክምና ለ ተመሠረተ መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት መኖሪያ ቤት የ Data-Driven Medicine ፍላጎት ያደርጋል በአብዛኛው ኢንዱስትሪዎች ውስጥ የ ML እና በተለይም የዲኤሌክትሮኒክ ዲዛይን (ዲኤሌክትሮኒክስ ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ዲዛይን ውሂብ ላይ ደህንነት 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. የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ የቴክኒካዊ , ወይም የንግድ ልማት እና የሳይንስ ልማት መሣሪያ እንደ, ለምሳሌ, የ NHS ስኮትላንድ ብሔራዊ ደህንነት ወደብ የፈረንሳይ የሕክምና Data Hub የዩናይትድ ስቴትስ የጤና Data Research . 21 22 23 24 Substantial, albeit smaller, initiatives include the Human Connectome , the UK Biobank የ Cancer Imaging Archive (TCIA) የ NIH CXR8 አግኝቷል DeepLesion , the Cancer Genome Atlas (TCGA) የ Alzheimer’s Disease Neuroimaging Initiative (ADNI) (የአልካይሜር ስኬት) (የአልካይሜር ስኬት) በተጨማሪም የቴክኒካዊ ፍላጎቶች such as the CAMELYON challenge , the International multimodal Brain Tumor Segmentation (BraTS) challenge , , የ Decathlon የሕክምና ክፍሎች የግል የሕክምና ውሂብ አብዛኛውን ጊዜ ተግባር ወይም ሕክምና ልዩ ነው እና በአብዛኛው ጊዜ የተለያዩ ደረጃዎች የክፍያ መስፈርቶች ጋር ይሰጣል, አንዳንድ ጊዜ የክፍያ መስፈርቶች ይሰጣል. 25 26 27 28 29 30 31 32 33 34 35 36 37 ነገር ግን ውሂብ ማከማቻ ወይም ውሂብ ማከማቻ በግልታ እና ውሂብ ጥበቃ ጋር የተወሰነ የተመሠረተ, የቴክኒካዊ, የቴክኒካዊ እና የቴክኒካዊ ፍላጎቶች ብቻ አይደለም, ነገር ግን ደግሞ የቴክኒካዊ ፍላጎቶች ያደርጋል. የግል መረጃ ማከማቻ, መዳረሻ መቆጣጠሪያ እና ደህንነቱ የተጠበቀ የጤና ውሂብ ማከማቻ አንድ የማይታመን, እና አንዳንድ ጊዜ አይችልም ተግባር ነው. በኤሌክትሮኒክ የጤና ውሂብ ውሂብ የተመሠረተ ውሂብ በ GDPR / PHI ተስማሚ ሊሆን ይችላል, ነገር ግን ብቻ ጥቂት ውሂብ ክፍሎች ደንበኞች ለመቀነስ ሊሆን ይችላል. ይህ ደግሞ genomic data እና የሕክምና ፎቶዎች ጋር ተስማሚ ነው. . 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 የፋይድሮሊክ እርምጃዎች በኤፍ.ኤፍ.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም.ኤም በ Fig. , አንድ FL workflow የተለያዩ topologies እና compute plans ጋር ሊሆን ይችላል. የሙከራ መተግበሪያዎች ለ በጣም ታዋቂ ሁለት አንድ aggregation server በኩል ነው. , , and peer to peer approaches , በኤሌክትሮኒክስ ኮምፒውተር (ኤሌክትሮኒክስ ኮምፒውተር) ውስጥ የፕላስቲክ ኮምፒውተር (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮኒክስ ኮምፒውተር) (ኤሌክትሮ , , , ለምሳሌ, የዲጂታል ደህንነት ማረጋገጫዎች , በአጠቃላይ, የአካባቢ ጥበቃ መተግበሪያዎች ላይ የ FL መተግበሪያ ችሎታ በ FL መተግበሪያዎች ውስጥ ደህንነት ለማሻሻል ይሰጣል (አጠቃላይ, የአካባቢ ጥበቃ መተግበሪያዎች ላይ የ FL መተግበሪያዎች ችሎታ በዩናይትድ ስቴትስ ውስጥ ትኩረት ይሰጣል). 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—federation አጠቃቀም መዋቅር. Centralised: the aggregation server coordinates the training iterations and collects, aggregates and distributes the models to and from the Training Nodes (Hub & Spoke). እያንዳንዱ training node አንድ ወይም ከሁሉም peers ጋር የተገናኙ ነው እና አጠቃቀም እያንዳንዱ node ላይ በተመሳሳይ ጊዜ ይሰራል. የፕሮጀክቶች እና የፕሮጀክቶች እና የፕሮጀክቶች እና የፕሮጀክቶች ( )). FL compute plans—trajectory of a model across several partners. የሲክሊካል የመግቢያ / የሲክሊካል የመግቢያ / የሲክሊካል የመግቢያ. Aggregation server, Peer to Peer. a b c d e f g Current FL efforts for digital health የፕላስቲክ አጠቃቀም (FL) የፕላስቲክ አጠቃቀም (AI) አጠቃቀም (AI) አጠቃቀም (AI) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም (FL) አጠቃቀም ለምሳሌ, የኤሌክትሮኒክ የሕክምና መለያዎች (EHR) ውስጥ, FL በክሊኒካል ተመሳሳይ ደንበኞች ለማምረት እና ማግኘት ይረዳል. , , እንዲሁም በክፍያ ተጽዕኖዎች ምክንያት በክፍያ ተጽዕኖዎችን ለማረጋገጥ የ ICU ጊዜ እና የ ICU ጊዜ የ FL ትክክለኛነት እና ፍላጎቶች ደግሞ በ MRI ውስጥ የሙዚቃ ቅርጸት ለማግኘት የሕክምና ቅርጸት መስመር ላይ የተረጋገጠ ናቸው. , as well as brain tumour segmentation , . 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. እነዚህን ያካትታሉ የኮንሰርቶሮች, እነርሱ ለማሻሻል ይፈልጋሉ የ Trustworthy Federated Data Analytics (TFDA) ፕሮጀክቶች and the German Cancer Consortium’s Joint Imaging Platform የጀርመን የሕክምና ፎቶነር ምርምር ተቋማት ውስጥ የተመሠረተ ምርምር ያደርጋል. አንድ ሌሎች ለምሳሌ የፈጠራ ሞዴሎች ለማረጋገጥ ለ FL በመጠቀም የዓለም አቀፍ ምርምር አጠቃቀም ነው . 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 የአገልግሎት ተቋማት ያካትታሉ, ይህም ምርምር ማዕከልዎች ብቻ አይደለም, FL በቀጥታ ሊሆን ይችላል ውጤታማነት. የሽያጭ HealthChain ፕሮጀክት , 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 በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬሽን) በኤሌክትሮኒክስ ኮርፖሬሽን (ኤሌክትሮኒክስ ኮርፖሬ clinical 52 53 ሌላ ገጽ መለያው ውስጥ ነው research and translation. FL enables collaborative research for, even competing, companies. In this context, one of the largest initiatives is the Melloddy project . 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 Impact on stakeholders 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. ሆቴሎች እና ተግባሮች በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴትስ ውስጥ በዩናይትድ ስቴት Researchers and AI developers የምስክር ወረቀቶች እና የቴክኒካዊ ምርምር ባለሙያዎች በአጠቃላይ የፈጠራ መረጃ አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም አጠቃቀም , , . 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 ብዙ አገሮች ውስጥ የሕክምና አቅራቢዎች በባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህር-ባህ 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. , ነገር ግን የተለያዩ ሌሎች ዝርዝሮች በኮምፒውተር ውስጥ የተመሠረተ ናቸው , , , አንድ FL workflow (አል. ) የተለያዩ ቶፖሎጂዎች እና የኮምፒውተር ቅርጸቶች በመጠቀም ሊሆን ይችላል (fig. በዚህ ክፍል ውስጥ, FL ምን ነው, እንዲሁም በዲጂታል ሕክምና ውስጥ FL መተግበሪያ ላይ የሚከተሉትን ዋና ፍላጎቶች እና የቴክኒካዊ ግምገማዎችን ያተኮሩ ይሆናል. 55 9 11 12 20 1 2 Federated learning definition FL በአጠቃላይ ቅርጸት FL እንደዚህ ይላል: በዓለም አቀፍ ፍላጎት ቅርጸት ለማግኘት በዓለም አቀፍ ፍላጎት ቅርጸት ሊሆን ይችላል. local losses , computed from private data , which is residing at the individual involved parties and never shared among them: K Xk እንዴት > 0 respective weight coefficients ያካትታል. wk በእርግጥ, እያንዳንዱ ተጫዋች በአካባቢው ላይ እና ከባድ ወይም በፓራሚተር ደንበኞች በኩል በመስመር ላይ ወይም በመስመር ላይ የቅርብ ጊዜዎችን ለማሳየት በፊት ከሁለት ጫማዎች ለማሻሻል በዓለም አቀፍ ጥንካሬ ሞዴል ለማግኘት እና ማሻሻል ይቻላል. ) , የፓራሚተሮች አጠቃቀም ውጤታማ ሂደት በኔትሪት ቶፖሎሎጂ ላይ የተመሠረተ ነው, ምክንያቱም ኔትሪቶች በይነገጽ ወይም ህጋዊ ጥንካሬዎች ምክንያት በይነገጽ በይነገጽ በይነገጽ ሊሆን ይችላል. ). 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. የኮምፒውተር ፕሮግራም 2 Challenges and considerations 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 የ Data Heterogeneity የሕክምና ውሂብ በጣም የተለያዩ ነው—በአጠቃላይ ሞዴሎች, መጠን እና ባህሪያት የተለያዩ ምክንያት ብቻ አይደለም, ነገር ግን እንደ መግዛት የተለያዩ, የሕክምና መሣሪያ ምርት ወይም አካባቢዊ የቴክኒካዊ ግምገማዎች እንደ ተጽዕኖዎች እንደ አንድ ልዩ ፕሮቶኮል ውስጥ ሊሆን ይችላል.ኤፍኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤምኤም are prone to fail under these conditions , , , in part defeating the very purpose of collaborative learning strategies. Recent results, however, indicate that FL training is still feasible , even if medical data is not uniformly distributed across the institutions , or includes a local bias . Research addressing this problem includes, for example, , part-data-sharing strategy እና FL በ Domain-Adaptation ጋር ሌላው ፍላጎት የግል መረጃ ንጥረነት በአጠቃላይ ትክክለኛ መፍትሔ በአጠቃላይ ትክክለኛ መፍትሔ በአጠቃላይ ትክክለኛ ተጫዋቾች ለማግኘት ትክክለኛ ሊሆን አይችልም ሁኔታ ሊሆን ይችላል. FedAvg 9 9 57 58 59 16 17 51 አግኙን 57 58 18 ደህንነት እና ደህንነት 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. የአገልግሎት vs. አፈጻጸም: ለ FL የተጠበቀ የግል መረጃ ሁሉንም የግል መረጃ ጥያቄዎች አይነቶች አይነቶች እና — በአጠቃላይ ለ ML መግቢያዎች ተመሳሳይ — በአጠቃላይ የተጠበቀ የግል መረጃ ጥበቃ ቴክኖሎጂዎች በአጠቃላይ በአጠቃላይ የንግድ ላይ ይገኛል ML ሞዴሎች ጋር ተመሳሳይ የግል መረጃ ጥበቃ ደረጃዎችን ያቀርባል. ቢሆንም, አፈጻጸም ውስጥ አንድ ትክክለኛነት ሊሆን ይችላል እና እነዚህ ቴክኖሎጂዎች, ለምሳሌ, መጨረሻ ሞዴል ትክክለኛነት ላይ ሊሆን ይችላል. በተጨማሪም, የአምላክ ቴክኖሎጂዎች እና / ወይም ተስማሚ መረጃዎች በአሁኑ ጊዜ ዝቅተኛ ጤናማ ሞዴሎች ሊሆን ይችላል. 12 10 የምስክር ወረቀት ደረጃ: በአጠቃላይ, ተጫዋቾች ሁለት ዓይነት FL ጥገና መውሰድ ይችላሉ: — ለኤፍ.ኤፍ.ኤስ. ኮንሰርቲሎች, በዚያ ሁሉንም ክፍሎች አስተማማማኝ ይሆናል እና በስተካከሉ የጋራ ልውውጥ ሂደቶች የተጠበቁ ናቸው, በባህላዊ መረጃ ማውረድ ወይም በባህላዊ ሞዴልን ለመርዳት እንደ አብዛኞቹ ከባህር ሞዴሎች መቁረጥ ይችላሉ. ይህ በባህላዊ የጋራ ልውውጥ መስፈርቶች ላይ ይመዝገቡ እና በባህላዊ ልውውጥ መስፈርቶች ላይ ይመዝገቡ. Trusted — በ FL ስርዓቶች ውስጥ በከፍተኛ መጠን ላይ ይሰራሉ, በተሳካ ጥቅል ተስማሚ ማህበራዊ ትዕዛዞች ለመፍጠር የማይታመን ሊሆን ይችላል. አንዳንድ ደንበኞች በተሳካ ሁኔታ አፈጻጸም ለመቀነስ, ስርዓት ለመቀነስ ወይም ከሌሎች ክፍሎች ከ መረጃ ማውረድ ይፈልጋሉ. ስለዚህ, እንደዚህ ጥቅሎች እንደ, የሞዴል መልዕክቶች የተሻሻለ የክፍያ, ሁሉም ክፍሎች የተጠበቀ የክፍያ, እንቅስቃሴዎች የሚከተሉነት, የተለያዩ ደህንነት, ማረጋገጫ ስርዓቶች, ትክክክለኛነት, ሞዴል ደህንነት እና መዳረሻዎች መከላከያ እንደ ደህንነት ፕሮግራሞች ይፈልጋሉ. 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 or adversarial attacks , FL የፈጠራ ስልጠና ሂደት ከብዙ አገሮች ጋር ተስማሚ ነው, ስለዚህ ከባድ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደብ-የመደ , and ensuring adequate differential privacy ሊሆን ይችላል እና በአሁኑ ጊዜ የፈጠራ አካባቢ ነው. . 60 61 62 63 16 18 44 12 ትክክለኛነት እና ትኩረት 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 Unlike running large-scale FL amongst consumer devices such as 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 እንደዚህ የኮርፖሬሽን አስተዳደር የተለያዩ መንገድ ሊሆን ይችላል. በገበያዎች መካከል በጣም ጠንካራ ውሂብ ደህንነት ያስፈልጋቸዋል ሁኔታዎች ውስጥ, ስልጠና በ "እውነተኛ ብሬክር" ስርዓት በኩል ሊሆን ይችላል, በዚያ አንድ አስተማማማኝ የቴክኒኮች እንደ መተግበሪያ ይሰራል እና ውሂብ መዳረሻ ይሰጣል. ይህ መተግበሪያ የኮርፖሬሽን ስርዓት ለመቆጣጠር አንድ አጠገብ ባለቤትነት ያስፈልጋል, ይህም ሁልጊዜ የሚፈልጉ አይችልም, ምክንያቱም ተጨማሪ ወጪዎች እና ሂደቶች viscosity ሊሆን ይችላል. ቢሆንም, ይህ የኮርፖሬሽን ስርዓቶች ከኮርፖሬሽን መተግበሪያዎች ከኮርፖሬሽን ከኮርፖሬሽን መተግበሪያዎች መጨረሻው ML, እና በእርግጥ DL, በዲጂታል የሕክምና መስመሮች ውስጥ የዲጂታል ሕክምና መስመሮች በስፋት መተግበሪያዎችን ያደርጋል. ሁሉም ML መተግበሪያዎች በአጠቃላይ በአጠቃላይ በአጠቃላይ ዓለም አቀፍ አጠቃቀም ጋር ተስማሚ ውሂብ ለማግኘት ችሎታ ከብዙ ክፍሎች ጋር ተስማሚ መተግበሪያዎችን ያደርጋል, FL የኃይል, ትክክክለኛ, ደህንነቱ የተጠበቀ, ጠንካራ እና የማይታመን ሞዴሎች ለማግኘት የፈጠራ መተግበሪያ ነው. በአጠቃላይ በአጠቃላይ ከብዙ ክፍሎች በግልነት ለመተግበሪያ ወይም ውሂብ መተግበሪያዎችን ለመተግበሪያ አይችልም, FL በተመሳሳይ ምርምር እና የንግድ መስመሮች ለመፍጠር እና በዓለም ቢሆንም, በእርግጥ የእኛን ትክክለኛነት መድኃኒት እና በአጠቃላይ የጤና ማሻሻያ ላይ ትክክለኛነት ውጤታማነት በጣም አስቸጋሪ ነው. 12 Reporting summary Further information on research design is available in the linked to this article. የምስክር ወረቀት Nature Research Report አስተያየቶች LeCun, Y., Bengio, Y. & Hinton, G. 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International Conference on Machine Learning የምስክርነት ይህ ሥራ በዩናይትድ ስቴትስ ምርምር እና Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, በ Wellcome/EPSRC Center for Medical Engineering (WT203148/Z/16/Z), በ Wellcome Flagship Programme (WT213038/Z/18/Z), በ National Institutes of Health (NIH) Clinical Center Intramural Research Programme (Project “Trustworthy Federated Data Analytics”) እና የጀርመን Academic Exchange Service (DAAD) PRIME ፕሮግራም በጀርመን Federal Ministry of Education and Research (BMBF) ክፍሎች ጋር በዩናይትድ ስቴትስ (DAAD) ክፍያ ቁጥር U01CA242871, በ NIH National Institute of Neurological Disorders and Stroke (WT213038/Z/18/Z) ክፍያ This paper is under CC by 4.0 Deed (Attribution 4.0 International) license. available on nature ይህ ገጽ under CC by 4.0 Deed (Attribution 4.0 International) license. available on nature
