Federated learning (FL) is vulnerable to intermittent data poisoning and unreliable updates from untrusted participants. Existing blockchain-aided FL schemes provide immutable logging of model updates but still face three limitations: lack of temporal adversary modeling, reliance on costly on-chain computation, and communication overhead that restricts scalability. We propose State-Space Model-based Truth Discovery (SSMTD), a unified framework that addresses these challenges by integrating: (1) a Hidden Markov filter that assigns epoch-wise soft reputations to capture time-varying client reliability, (2) off-chain oracles that compute lightweight gradient-consistency metrics to avoid prohibitive gas costs, and (3) a decentralized oracle design that ensures robustness while maintaining sublinear communication growth as the number of clients increases. The inferred reputations are anchored on-chain. These reputations enable automated reward–penalty contracts that re-weight or exclude malicious participants in subsequent aggregation rounds. This combination of temporal reliability modeling, oracle-assisted off-chain computation, and scalable decentralized verification distinguishes SSMTD from prior approaches, which either neglect adversary dynamics or incur prohibitive overhead. Experiments on FMNIST, CIFAR-10, and LEAF under varying attack intensities and noise conditions show that SSMTD consistently outperforms reputation-based baselines, achieving 2.6–11.9 percentage-point improvements in F1-scores alongside simultaneous gains in precision and recall. System profiling further confirms stable throughput, sublinear latency growth, and minimal resource overhead, demonstrating strong scalability.
Li et al. (Mon,) studied this question.