A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces

The evaluated cost structure is amortizing because substantial deployment expenditure is paid per hired trainer only at setup, while recurring expenditure is markedly smaller and becomes diluted over the federation lifetime.

Source: [10], [12]

Lifecycle-level cost decomposition and amortization analysis provide a basis for assessing decentralized coordination architectures beyond isolated transaction measurements.

Source: [18]

The study asks whether marketplace operating cost is chiefly a fixed deployment burden or a recurring burden that accumulates through training.

Source: [8]

The evaluated design joins compatible smart contracts with decentralized storage and a federated-learning framework.

Source: [25]

Coordination rules are executed autonomously through the contract layer.

Source: [5]

The lifecycle separates a per-trainer setup stage from a training loop that recurs across communication rounds.

Source: [23]

Settlement proceeds asynchronously through a layered transaction arrangement with periodic base-layer commitments.

Source: [5]

The implementation uses local contract execution and local decentralized storage to provide deterministic execution and controlled artifact availability.

Source: [21]

The baseline is a conventional federated-averaging deployment without blockchain interaction or decentralized storage.

Source: [6]

The intermediate configuration retains contract coordination while substituting direct deterministic hashes for storage uploads, whereas the full configuration includes both coordination and decentralized storage.

Source: [6]

The experiments use a compact image-classification workload and federated averaging, with separate ablation and scaling settings and repeated randomized runs.

Source: [6], [13]

The evaluation covers lifecycle gas use, round execution time, final predictive performance, matching scalability, and deployment-cost amortization.

Source: [18], [26]

The measured fixed setup component for the experimental federation is reported in the supporting quantitative record.

Source: [11]

The recurring communication-round component for that federation is reported separately in the supporting quantitative record.

Source: [4]

Final predictive results were reported for the baseline, coordination-only, and full configurations after the evaluated training schedule.

Source: [3], [9]

The evaluated configurations reached statistically indistinguishable final accuracy, indicating preserved learning outcomes within this experiment.

Source: [2]

Lifecycle gas consumption per hired trainer was concentrated in initial deployment, especially participant onboarding and offer acceptance.

Source: [10]

For the experimental federation, the amortization knee is defined where average round cost reaches a specified multiple of the long-run recurring cost.

Source: [16], [27]

Reported average round durations were closely aligned across the evaluated configurations.

Source: [20], [22]

Across the tested range of hired trainers, the amortization knee remains bounded and approaches a limiting value under the measured regime.

Source: [1]

The amortization model expresses average operating cost by separating fixed setup expenditure from recurring round expenditure over the operational horizon.

Source: [14], [24]

The amortization finding assumes the measured offer-availability regime at acceptance, and alternative matching regimes were not evaluated.

Source: [7], [19]

Accuracy preservation is limited to the compact workload and coordination task evaluated, rather than establishing generality across task difficulty or model scale.

Source: [7], [19]

Monetary cost depends on the target network, and local storage results do not include variability associated with public storage gateways.

Source: [21]