Deviation-Responsive Contracts for Federated Learning: Combining Prospect Theory, Reciprocity, and DRO

Most FL contract designs assume rational best-responses under expected utility with static types and IR/IC constraints (e.g., Tian et al., 2021; Li et al., 2023). Fu et al. (2023) take an important step by modeling the principal’s risk attitudes via prospect theory, but the agent side is still largely treated as EUT-consistent. Empirically, however, FL agents often deviate for reasons beyond pure payoff—reciprocity norms, fairness concerns, and bounded rationality (echoed by consumer reciprocity findings in rollover contracts by Wilkins et al., 2024). This idea proposes a dynamic menu of contracts for FL that (a) models both principal and agent using prospect theory to capture loss aversion and probability weighting; (b) incorporates reciprocity “credits” that reward cooperative gestures (e.g., sudden bursts of effort or voluntary validation checks) and penalize opportunism over time; and (c) hedges against misspecified behavioral parameters with distributionally robust optimization, à la Zhan et al. (2025), to protect performance under uncertainty and information asymmetry. We also incorporate strong-asymmetry features and correlated contributions (Wang et al., 2024) by learning deviation patterns from interaction histories and adjusting the menu online. The novelty is twofold: (1) treating deviations from EUT as first-class design objects rather than anomalies to be averaged away; and (2) combining behavioral modeling with DRO to preserve guarantees when behavioral parameters drift. If successful, this could deliver FL mechanisms that are both faster to converge and more resistant to collusion/free-riding than classic IR/IC-only menus (cf. Li et al., 2023), with measurable improvements in participation stability under realistic behavioral noise.

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