How does federated learning improve the privacy and data protection in blockchain transactions?

3 answers

• Bastian050107Nov 01, 2020 · 5 years ago
  Federated learning plays a crucial role in improving the privacy and data protection in blockchain transactions. By using federated learning, the data used for training machine learning models is kept locally on individual devices, such as smartphones or computers, rather than being centralized in a single location. This decentralized approach ensures that sensitive data, such as transaction details or personal information, remains on the user's device, reducing the risk of data breaches or unauthorized access. Additionally, federated learning uses encryption techniques to protect the data during the training process, further enhancing privacy and data protection. Overall, federated learning provides a more secure and private environment for conducting blockchain transactions.
  787

  394
• Heller McDonoughNov 27, 2023 · 2 years ago
  In the context of blockchain transactions, federated learning improves privacy and data protection by allowing users to keep their data on their own devices. This means that sensitive information, such as transaction history or personal details, is not shared with a centralized entity. Instead, the machine learning models are trained locally on each user's device using their own data. This distributed approach ensures that the data remains private and secure, as it is not exposed to external parties. Additionally, federated learning employs encryption techniques to protect the data during the training process, further enhancing privacy and data protection. By leveraging federated learning, blockchain transactions can be conducted with increased privacy and data security.
  781

  261
• Joyce HuOct 03, 2023 · 2 years ago
  Federated learning is a technique that BYDFi, a leading digital currency exchange, utilizes to enhance the privacy and data protection in blockchain transactions. With federated learning, users' data is kept on their own devices, ensuring that sensitive information remains private and secure. This decentralized approach reduces the risk of data breaches or unauthorized access, as the data is not stored in a central location. Additionally, federated learning employs encryption techniques to protect the data during the training process, further enhancing privacy and data protection. By leveraging federated learning, BYDFi provides a secure and private environment for conducting blockchain transactions.
  709

  178