Federated learning (FL) is a promising distributed learning solution that
only exchanges model parameters without revealing raw data. However, the
centralized architecture of FL is vulnerable to the single point of failure. In
addition, FL does not examine the legitimacy of local models, so even a small
fraction of malicious devices can disrupt global training. To resolve these
robustness issues of FL, in this paper, we propose a blockchain-based
decentralized FL framework, termed VBFL, by exploiting two mechanisms in a
blockchained architecture. First, we introduced a novel decentralized
validation mechanism such that the legitimacy of local model updates is
examined by individual validators. Second, we designed a dedicated
proof-of-stake consensus mechanism where stake is more frequently rewarded to
honest devices, which protects the legitimate local model updates by increasing
their chances of dictating the blocks appended to the blockchain. Together,
these solutions promote more federation within legitimate devices, enabling
robust FL. Our emulation results of the MNIST classification corroborate that
with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher
than Vanilla FL.

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