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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Blockchain-Based Federated Learning System: A Survey on Design Choices.

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Federated learning in untrusted settings benefits from blockchain integration. This survey analyzes blockchain-based federated learning designs, revealing trade-offs between fairness, robustness, and efficiency, highlighting areas for future research.

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Cybersecurity

Background:

  • Federated learning (FL) traditionally assumes trusted environments, limiting its application in real-world scenarios requiring collaboration among untrusted parties.
  • Blockchain technology offers a decentralized and immutable platform, making it a promising solution for enhancing trust in federated learning systems.
  • Recent research interest has focused on integrating blockchain with federated learning to address security and trust concerns.

Purpose of the Study:

  • To conduct a comprehensive literature survey of state-of-the-art blockchain-based federated learning (BFL) systems.
  • To analyze common design patterns and variations used to overcome challenges in BFL.
  • To evaluate the pros and cons of different design choices based on key performance metrics.

Main Methods:

  • Systematic literature review of existing BFL research.
  • Identification and categorization of approximately 31 distinct design variations across BFL systems.
  • Analysis of design patterns considering robustness, efficiency, privacy, and fairness.

Main Results:

  • A linear relationship was observed between fairness and robustness, indicating that improvements in fairness can enhance robustness.
  • A trade-off exists between improving all metrics simultaneously and overall system efficiency.
  • Popular design choices and areas needing further development in BFL were identified.

Conclusions:

  • Future BFL systems require advancements in model compression and asynchronous aggregation techniques.
  • There is a need for more rigorous system efficiency evaluations in BFL research.
  • Further research should focus on applying BFL to cross-device settings and addressing identified efficiency trade-offs.