The paper presents an innovative approach to address the challenges of
scalability and reliability in Distributed Federated Learning by leveraging the
integration of blockchain technology. The paper focuses on enhancing the
trustworthiness of participating nodes through a trust penalization mechanism
while also enabling asynchronous functionality for efficient and robust model
updates. By combining Semi-Decentralized Federated Learning with Blockchain
(SDFL-B), the proposed system aims to create a fair, secure and transparent
environment for collaborative machine learning without compromising data
privacy. The research presents a comprehensive system architecture,
methodologies, experimental results, and discussions that demonstrate the
advantages of this novel approach in fostering scalable and reliable SDFL-B
systems.

借助区块链技术，本研究提出了一种创新方法来解决分布式联邦学习中的可伸缩性和可靠性挑战，通过整合区块链技术来增强参与节点的可信度，同时实现异步功能以实现高效且坚固的模型更新。通过将半集中式联邦学习与区块链相结合，该系统旨在创建一个公平、安全和透明的环境，以促进合作机器学习，同时不损害数据隐私。研究提供了一个全面的系统架构、方法论、实验结果和讨论，展示了这种新方法在促进可扩展和可靠的半集中式联邦学习 - 区块链系统方面的优势。

提高区块链中半分散化联合学习的可伸缩性和可靠性：信任惩罚与异步功能

Enhancing Scalability and Reliability in Semi-Decentralized Federated  Learning With Blockchain: Trust Penalization and Asynchronous Functionality

We present a semi-decentralized federated learning algorithm wherein clients
collaborate by relaying their neighbors' local updates to a central parameter
server (PS). At every communication round to the PS, each client computes a
local consensus of the updates from its neighboring clients and eventually
transmits a weighted average of its own update and those of its neighbors to
the PS. We appropriately optimize these averaging weights to ensure that the
global update at the PS is unbiased and to reduce the variance of the global
update at the PS, consequently improving the rate of convergence. Numerical
simulations substantiate our theoretical claims and demonstrate settings with
intermittent connectivity between the clients and the PS, where our proposed
algorithm shows an improved convergence rate and accuracy in comparison with
the federated averaging algorithm.

提出一种半分散式联邦学习算法，在其中客户端通过中央参数服务器（PS）中继其邻居的本地更新进行合作，以实现全局更新，有效提高收敛速度和精度。