  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.
