当联邦学习遇到区块链：一种新的分布式学习范式
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
摘要：
受终端用户设备爆炸性计算能力以及对共享敏感原始数据日益增长的隐私担忧的推动，出现了一种名为联邦学习（FL）的新机器学习范式。通过在每个客户端本地训练模型并在中央服务器上聚合学习模型，FL能够避免直接共享数据，从而减少隐私泄露。然而，传统的FL框架严重依赖于单个中央服务器，并且如果这样的服务器恶意行为，则可能会崩溃。为了解决这一单点故障问题，本文研究了一种区块链辅助的去中心化FL（BLADE-FL）框架，该框架可以很好地防止恶意客户端毒化学习过程，并进一步为客户端提供一个自我激励和可靠的学习环境。具体来说，模型聚合过程是完全分散的，FL培训和区块链挖掘的任务被集成到每个参与者中。此外，我们调查了这个框架中的独特问题，并提供了分析和实验结果，以阐明可能的解决方案。
英文摘要：
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the traditional FL framework heavily relies on a single central server and may fall apart if such a server behaves maliciously. To address this single point of failure issue, this work investigates a blockchain assisted decentralized FL (BLADE-FL) framework, which can well prevent the malicious clients from poisoning the learning process, and further provides a self-motivated and reliable learning environment for clients. In detail, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. In addition, we investigate the unique issues in this framework and provide analytical and experimental results to shed light on possible solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
