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Response
200 OK
IP
216.73.216.131
Profiled on
Token
a295cd

Forms

  • sylius_add_to_cart
    • cartItem
    • _token

sylius_add_to_cart

Form type: 
"Sylius\Bundle\CoreBundle\Form\Type\Order\AddToCartType"

Errors

This form has no errors.

Default Data

Property Value
Model Format same as normalized format
Normalized Format 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106970
  -cart: App\Entity\Order\Order {#13374 …}
  -cartItem: App\Entity\Order\OrderItem {#106958
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106982 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106983 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8104
      #id: 8002
      #code: "IEEE00007453PDF"
      #product: App\Entity\Product\Product {#7311
        #id: 12324
        #code: "IEEE00007453"
        #attributes: Doctrine\ORM\PersistentCollection {#7701 …}
        #variants: Doctrine\ORM\PersistentCollection {#7744 …}
        #options: Doctrine\ORM\PersistentCollection {#7916
          #collection: Doctrine\Common\Collections\ArrayCollection {#7918 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7311}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#200015 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7900 …}
        #createdAt: DateTime @1751040358 {#7274
          date: 2025-06-27 18:05:58.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1753970953 {#7322
          date: 2025-07-31 16:09:13.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7922 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7921
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7311}
            #id: 44305
            #name: "IEEE 3652.1:2020"
            #slug: "ieee-3652-1-2020-ieee00007453-243977"
            #description: """
              New IEEE Standard - Active.<br />\n
              Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.<br />\n
              \t\t\t\t<br />\n
              Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.<br />\n
              Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. Federated machine learning is expected to promote and facilitate collaborations among multiple parties, some of which are data source owners, such that user privacy and information security are protected. This guide will promote the use of distributed data sources without violating regulations or ethical considerations.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Guide for Architectural Framework and Application of Federated Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7534 …}
        #channels: Doctrine\ORM\PersistentCollection {#7628 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7309 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7613 …}
        #averageRating: 0.0
        #images: Doctrine\ORM\PersistentCollection {#7645 …}
        -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7324 …}
        -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7321 …}
        -apiLastModifiedAt: DateTime @1743289200 {#7317
          date: 2025-03-30 00:00:00.0 Europe/Paris (+01:00)
        }
        -lastUpdatedAt: DateTime @1622671200 {#7292
          date: 2021-06-03 00:00:00.0 Europe/Paris (+02:00)
        }
        -author: ""
        -publishedAt: DateTime @1616108400 {#7318
          date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
        }
        -releasedAt: null
        -confirmedAt: null
        -canceledAt: DateTime @1616108400 {#7316
          date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
        }
        -edition: null
        -coreDocument: "3652.1"
        -bookCollection: ""
        -pageCount: 69
        -documents: Doctrine\ORM\PersistentCollection {#7465 …}
        -favorites: Doctrine\ORM\PersistentCollection {#7500 …}
      }
      #optionValues: Doctrine\ORM\PersistentCollection {#8320 …}
      #position: 0
      #createdAt: DateTime @1751041343 {#7283
        date: 2025-06-27 18:22:23.0 Europe/Paris (+02:00)
      }
      #updatedAt: DateTime @1755612026 {#8121
        date: 2025-08-19 16:00:26.0 Europe/Paris (+02:00)
      }
      #enabled: true
      #translations: Doctrine\ORM\PersistentCollection {#8264 …}
      #translationsCache: [
        "en_US" => App\Entity\Product\ProductVariantTranslation {#93381
          #locale: "en_US"
          #translatable: App\Entity\Product\ProductVariant {#8104}
          #id: 8035
          #name: null
          -shortDescription: null
          -description: null
          -notes: null
          -shippingInformation: "Instant download"
        }
      ]
      #currentLocale: "en_US"
      #currentTranslation: null
      #fallbackLocale: "en_US"
      #version: 9
      #onHold: 0
      #onHand: 0
      #tracked: false
      #weight: 0.0
      #width: null
      #height: null
      #depth: null
      #taxCategory: Proxies\__CG__\App\Entity\Taxation\TaxCategory {#8136 …}
      #shippingCategory: null
      #channelPricings: Doctrine\ORM\PersistentCollection {#8298 …}
      #shippingRequired: true
      #images: Doctrine\ORM\PersistentCollection {#8295 …}
      -apiLastModifiedAt: DateTime @1753740000 {#8103
        date: 2025-07-29 00:00:00.0 Europe/Paris (+02:00)
      }
      -publishedAt: null
      -isbn: "978-1-5044-7053-7"
      -ean: "9781504470537"
      -numberOfUsers: 1
      -physicalProduct: false
      -downloadableImmediately: true
      -downloadable: true
      -drmViewerUrl: "https://online-viewer.normadoc.com/78x7wg"
      -sellable: true
      -documents: Doctrine\ORM\PersistentCollection {#8132 …}
      -drmTokens: Doctrine\ORM\PersistentCollection {#8124 …}
      -enabledForSubscribers: true
      -currentAreaContext: null
    }
    #productName: null
    #variantName: null
  }
}
View Format same as normalized format

Submitted Data

This form was not submitted.

Passed Options

Option Passed Value Resolved Value
data 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106970
  -cart: App\Entity\Order\Order {#13374 …}
  -cartItem: App\Entity\Order\OrderItem {#106958
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106982 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106983 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8104
      #id: 8002
      #code: "IEEE00007453PDF"
      #product: App\Entity\Product\Product {#7311
        #id: 12324
        #code: "IEEE00007453"
        #attributes: Doctrine\ORM\PersistentCollection {#7701 …}
        #variants: Doctrine\ORM\PersistentCollection {#7744 …}
        #options: Doctrine\ORM\PersistentCollection {#7916
          #collection: Doctrine\Common\Collections\ArrayCollection {#7918 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7311}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#200015 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7900 …}
        #createdAt: DateTime @1751040358 {#7274
          date: 2025-06-27 18:05:58.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1753970953 {#7322
          date: 2025-07-31 16:09:13.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7922 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7921
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7311}
            #id: 44305
            #name: "IEEE 3652.1:2020"
            #slug: "ieee-3652-1-2020-ieee00007453-243977"
            #description: """
              New IEEE Standard - Active.<br />\n
              Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.<br />\n
              \t\t\t\t<br />\n
              Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.<br />\n
              Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. Federated machine learning is expected to promote and facilitate collaborations among multiple parties, some of which are data source owners, such that user privacy and information security are protected. This guide will promote the use of distributed data sources without violating regulations or ethical considerations.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Guide for Architectural Framework and Application of Federated Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7534 …}
        #channels: Doctrine\ORM\PersistentCollection {#7628 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7309 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7613 …}
        #averageRating: 0.0
        #images: Doctrine\ORM\PersistentCollection {#7645 …}
        -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7324 …}
        -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7321 …}
        -apiLastModifiedAt: DateTime @1743289200 {#7317
          date: 2025-03-30 00:00:00.0 Europe/Paris (+01:00)
        }
        -lastUpdatedAt: DateTime @1622671200 {#7292
          date: 2021-06-03 00:00:00.0 Europe/Paris (+02:00)
        }
        -author: ""
        -publishedAt: DateTime @1616108400 {#7318
          date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
        }
        -releasedAt: null
        -confirmedAt: null
        -canceledAt: DateTime @1616108400 {#7316
          date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
        }
        -edition: null
        -coreDocument: "3652.1"
        -bookCollection: ""
        -pageCount: 69
        -documents: Doctrine\ORM\PersistentCollection {#7465 …}
        -favorites: Doctrine\ORM\PersistentCollection {#7500 …}
      }
      #optionValues: Doctrine\ORM\PersistentCollection {#8320 …}
      #position: 0
      #createdAt: DateTime @1751041343 {#7283
        date: 2025-06-27 18:22:23.0 Europe/Paris (+02:00)
      }
      #updatedAt: DateTime @1755612026 {#8121
        date: 2025-08-19 16:00:26.0 Europe/Paris (+02:00)
      }
      #enabled: true
      #translations: Doctrine\ORM\PersistentCollection {#8264 …}
      #translationsCache: [
        "en_US" => App\Entity\Product\ProductVariantTranslation {#93381
          #locale: "en_US"
          #translatable: App\Entity\Product\ProductVariant {#8104}
          #id: 8035
          #name: null
          -shortDescription: null
          -description: null
          -notes: null
          -shippingInformation: "Instant download"
        }
      ]
      #currentLocale: "en_US"
      #currentTranslation: null
      #fallbackLocale: "en_US"
      #version: 9
      #onHold: 0
      #onHand: 0
      #tracked: false
      #weight: 0.0
      #width: null
      #height: null
      #depth: null
      #taxCategory: Proxies\__CG__\App\Entity\Taxation\TaxCategory {#8136 …}
      #shippingCategory: null
      #channelPricings: Doctrine\ORM\PersistentCollection {#8298 …}
      #shippingRequired: true
      #images: Doctrine\ORM\PersistentCollection {#8295 …}
      -apiLastModifiedAt: DateTime @1753740000 {#8103
        date: 2025-07-29 00:00:00.0 Europe/Paris (+02:00)
      }
      -publishedAt: null
      -isbn: "978-1-5044-7053-7"
      -ean: "9781504470537"
      -numberOfUsers: 1
      -physicalProduct: false
      -downloadableImmediately: true
      -downloadable: true
      -drmViewerUrl: "https://online-viewer.normadoc.com/78x7wg"
      -sellable: true
      -documents: Doctrine\ORM\PersistentCollection {#8132 …}
      -drmTokens: Doctrine\ORM\PersistentCollection {#8124 …}
      -enabledForSubscribers: true
      -currentAreaContext: null
    }
    #productName: null
    #variantName: null
  }
}
 same as passed value
product 
App\Entity\Product\Product {#7311
  #id: 12324
  #code: "IEEE00007453"
  #attributes: Doctrine\ORM\PersistentCollection {#7701 …}
  #variants: Doctrine\ORM\PersistentCollection {#7744 …}
  #options: Doctrine\ORM\PersistentCollection {#7916
    #collection: Doctrine\Common\Collections\ArrayCollection {#7918 …}
    #initialized: true
    -snapshot: [ …4]
    -owner: App\Entity\Product\Product {#7311}
    -association: [ …21]
    -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
    -backRefFieldName: null
    -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#200015 …}
    -isDirty: false
  }
  #associations: Doctrine\ORM\PersistentCollection {#7900 …}
  #createdAt: DateTime @1751040358 {#7274
    date: 2025-06-27 18:05:58.0 Europe/Paris (+02:00)
  }
  #updatedAt: DateTime @1753970953 {#7322
    date: 2025-07-31 16:09:13.0 Europe/Paris (+02:00)
  }
  #enabled: true
  #translations: Doctrine\ORM\PersistentCollection {#7922 …}
  #translationsCache: [
    "en_US" => App\Entity\Product\ProductTranslation {#7921
      #locale: "en_US"
      #translatable: App\Entity\Product\Product {#7311}
      #id: 44305
      #name: "IEEE 3652.1:2020"
      #slug: "ieee-3652-1-2020-ieee00007453-243977"
      #description: """
        New IEEE Standard - Active.<br />\n
        Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.<br />\n
        \t\t\t\t<br />\n
        Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.<br />\n
        Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. Federated machine learning is expected to promote and facilitate collaborations among multiple parties, some of which are data source owners, such that user privacy and information security are protected. This guide will promote the use of distributed data sources without violating regulations or ethical considerations.
        """
      #metaKeywords: null
      #metaDescription: null
      #shortDescription: "IEEE Guide for Architectural Framework and Application of Federated Machine Learning"
      -notes: "Active"
    }
  ]
  #currentLocale: "en_US"
  #currentTranslation: null
  #fallbackLocale: "en_US"
  #variantSelectionMethod: "match"
  #productTaxons: Doctrine\ORM\PersistentCollection {#7534 …}
  #channels: Doctrine\ORM\PersistentCollection {#7628 …}
  #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7309 …}
  #reviews: Doctrine\ORM\PersistentCollection {#7613 …}
  #averageRating: 0.0
  #images: Doctrine\ORM\PersistentCollection {#7645 …}
  -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7324 …}
  -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7321 …}
  -apiLastModifiedAt: DateTime @1743289200 {#7317
    date: 2025-03-30 00:00:00.0 Europe/Paris (+01:00)
  }
  -lastUpdatedAt: DateTime @1622671200 {#7292
    date: 2021-06-03 00:00:00.0 Europe/Paris (+02:00)
  }
  -author: ""
  -publishedAt: DateTime @1616108400 {#7318
    date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
  }
  -releasedAt: null
  -confirmedAt: null
  -canceledAt: DateTime @1616108400 {#7316
    date: 2021-03-19 00:00:00.0 Europe/Paris (+01:00)
  }
  -edition: null
  -coreDocument: "3652.1"
  -bookCollection: ""
  -pageCount: 69
  -documents: Doctrine\ORM\PersistentCollection {#7465 …}
  -favorites: Doctrine\ORM\PersistentCollection {#7500 …}
}
 same as passed value

Resolved Options

Option Value
action 
""
allow_extra_fields 
false
allow_file_upload 
false
attr 
[]
attr_translation_parameters 
[]
auto_initialize 
true
block_name 
null
block_prefix 
null
by_reference 
true
compound 
true
constraints 
[]
csrf_field_name 
"_token"
csrf_message 
"The CSRF token is invalid. Please try to resubmit the form."
csrf_protection 
true
csrf_token_id 
null
csrf_token_manager 
Symfony\Component\Security\Csrf\CsrfTokenManager {#106990
  -generator: Symfony\Component\Security\Csrf\TokenGenerator\UriSafeTokenGenerator {#106991 …}
  -storage: Symfony\Component\Security\Csrf\TokenStorage\SessionTokenStorage {#106992 …}
  -namespace: Closure() {#106994 …}
}
data 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106970
  -cart: App\Entity\Order\Order {#13374 …}
  -cartItem: App\Entity\Order\OrderItem {#106958
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106982 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106983 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8104
      #id: 8002
      #code: "IEEE00007453PDF"
      #product: App\Entity\Product\Product {#7311
        #id: 12324
        #code: "IEEE00007453"
        #attributes: Doctrine\ORM\PersistentCollection {#7701 …}
        #variants: Doctrine\ORM\PersistentCollection {#7744 …}
        #options: Doctrine\ORM\PersistentCollection {#7916
          #collection: Doctrine\Common\Collections\ArrayCollection {#7918 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7311}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#200015 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7900 …}
        #createdAt: DateTime @1751040358 {#7274
          date: 2025-06-27 18:05:58.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1753970953 {#7322
          date: 2025-07-31 16:09:13.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7922 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7921
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7311}
            #id: 44305
            #name: "IEEE 3652.1:2020"
            #slug: "ieee-3652-1-2020-ieee00007453-243977"
            #description: """
              New IEEE Standard - Active.<br />\n
              Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.<br />\n
              \t\t\t\t<br />\n
              Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.<br />\n
              Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. Federated machine learning is expected to promote and facilitate collaborations among multiple parties, some of which are data source owners, such that user privacy and information security are protected. This guide will promote the use of distributed data sources without violating regulations or ethical considerations.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Guide for Architectural Framework and Application of Federated Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7534 …}
        #channels: Doctrine\ORM\PersistentCollection {#7628 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7309 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7613 …}
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      -drmTokens: Doctrine\ORM\PersistentCollection {#8124 …}
      -enabledForSubscribers: true
      -currentAreaContext: null
    }
    #productName: null
    #variantName: null
  }
}