Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate model aggregations and remains the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other energy-wasting but meaningless calculations. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness.

翻译：借助移动边缘计算的支撑，客户端-边缘-云端分层联邦学习（HFL）虽能在大范围区域内实现通信高效的模型训练，但中间模型聚合环节会引入额外的安全与隐私挑战，并仍存在单点故障问题。针对这些挑战，本文提出基于区块链的分层联邦学习（BHFL）系统，该系统在边缘服务器间运行许可型区块链进行模型聚合，无需中心化云服务器。然而，区块链的引入带来了额外开销。为实现紧凑高效的工作流程，我们设计了一种名为“联邦边缘学习证明”（PoFEL）的新型轻量级共识算法，通过回收局部模型训练所消耗的能量来提升效率。具体而言，该算法通过评估所有边缘服务器的中间联邦边缘学习模型来选择领导者节点，而非采用其他耗能且无意义的计算方式。相较于传统BHFL框架，这一设计显著提升了系统效率。为防止共识过程中的模型抄袭与贿赂投票行为，我们提出了基于哈希的承诺与数字签名（HCDS）方案及基于贝叶斯真话血清的投票（BTSV）方案。最后，我们设计了一种激励机制，鼓励客户端持续为学习任务贡献力量。实验结果表明，本文提出的BHFL系统及其配套的共识协议与激励机制在有效性、低计算成本和公平性方面均表现优异。