• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Biscotti: A Blockchain System for Private and Secure Federated Learning
【24h】

Biscotti: A Blockchain System for Private and Secure Federated Learning

机译：Biscotti：私人和安全联合学习的区块链系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Federated Learning is the current state-of-the-art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to both protect the privacy of an individual client's update and maintain the performance of the global model at scale when 30 percent adversaries are present in the system.
机译：联合学习是当前支持安全多方机器学习（ML）的最先进：数据维护在所有者的设备上，并通过安全协议聚合到模型的更新。但是，此过程假定协调的可信集中式基础架构，客户端必须相信中央服务不使用客户端数据的副产品。除此之外，一群恶意客户还可以通过进行中毒攻击来损害模型的性能。作为回应，我们提出了Biscotti：对不同聚会ML的对等（P2P）方法完全分散的对等体，它使用区块链和加密原语来协调凝视客户之间的隐私保留ML过程。我们的评价表明，Biscotti是可扩展的，容忍，并防止已知的攻击。例如，Biscotti能够保护个人客户更新的隐私，并在系统中存在30％的对手时保持全球模型的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号