【Author】
Moudoud, Hajar; Cherkaoui, Soumaya; Khoukhi, Lyes
【Source】2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
【Abstract】Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.
【Keywords】Federated learning; Blockchain; Sharding; reliability; Secure; Scalable
【摘要】联邦学习 (FL) 是一种分布式机器学习 (ML) 技术，它支持协作训练，其中设备使用本地数据集执行学习，同时保护其隐私。该技术可确保隐私、通信效率和资源节约。尽管有这些优势，但 FL 仍然面临与可靠性（即训练中不可靠的参与设备）、易处理性（即大量训练模型）和匿名性相关的挑战。为了解决这些问题，我们提出了一个为 FL 量身定制的安全可信的区块链框架（SRB-FL），它使用区块链特性以完全分布式和可信的方式实现协作模型训练。特别是，我们设计了一个基于区块链分片的安全 FL，以确保数据的可靠性、可扩展性和可信赖性。此外，我们引入了一种激励机制，以使用主观多权重逻辑提高 FL 设备的可靠性。结果表明，我们提出的 SRB-FL 框架高效且可扩展，使其成为联邦学习的有前途且合适的解决方案。
【关键词】联邦学习；区块链；分片；可靠性;安全的;可扩展
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