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Facilitated by mobile edge computing, client-edge-cloud hierarchicalfederated learning (HFL) enables communication-efficient model training in awidespread area but also incurs additional security and privacy challenges fromintermediate model aggregations and remains the single point of failure issue.To tackle these challenges, we propose a blockchain-based HFL (BHFL) systemthat operates a permissioned blockchain among edge servers for modelaggregation without the need for a centralized cloud server. The employment ofblockchain, however, introduces additional overhead. To enable a compact andefficient workflow, we design a novel lightweight consensus algorithm, namedProof of Federated Edge Learning (PoFEL), to recycle the energy consumed forlocal model training. Specifically, the leader node is selected by evaluatingthe intermediate FEL models from all edge servers instead of otherenergy-wasting but meaningless calculations. This design thus improves thesystem efficiency compared with traditional BHFL frameworks. To prevent modelplagiarism and bribery voting during the consensus process, we proposeHash-based Commitment and Digital Signature (HCDS) and Bayesian TruthSerum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism tomotivate continuous contributions from clients to the learning task.Experimental results demonstrate that our proposed BHFL system with thecorresponding consensus protocol and incentive mechanism achieveseffectiveness, low computational cost, and fairness.

PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning