Decentralised federated learning removes the central coordinator but requires participants to establish model update integrity autonomously. Existing frameworks either use inexpensive yet unverifiable proxies, including dataset size and epoch count, or validate updates through retraining, which is computationally costly. This paper introduces Proof of Contribution Quality (PoCQ), a framework in which trust is based on published evidence rather than on private judgement. Updates are committed before disclosure and assessed by small assigned peer committees against the updates each committee already holds. No common validation dataset is required, no validator retrains the evaluated model, and no participant requires a view of all updates in a round. Every vote is published with the values that produced it, so any peer can recompute it and contradict a dishonest validator. Accountability is graded by the strength of that evidence, with permanent removal reserved for provable misconduct and bounded suspensions for statistical anomalies, which honest nodes can produce under non-IID data. Across five model poisoning attacks on PathMNIST, PoCQ improves the average update level detection precision of the best current framework by 49% under non-IID data and by 206% under IID, and improves the precision of malicious node detection by 106% under non-IID data. This is achieved while reducing the time required to validate by half against the fastest existing framework and by 92% against retraining based validation. Colluding bad-mouthing validators are removed by recomputing their published evidence, and free riders who replay or copy an update exactly are identified directly from the ledger.

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