FLAIRE: Zero-Trust and Rarity-Aware Client Incentivization for Heterogenous Federated Learning

A challenge in Federated Learning is training under severe non-IID label skew. Incentivizing Rare-label clients is essential for maintaining global performance Existing incentive mechanisms often break down in practical deployments: they either assume honest client self-reporting of descriptors—exposing the system to strategic manipulation—or rely on server-side auxiliary datasets and external validators, violating core privacy and practical constraints. To resolve this dilemma, we propose FLAIRE (Federated Learning with Adaptive Incentives and Rewards Engine), an incentive architecture for heterogeneous FL built upon a zero-trust, zero-auxiliary-data, and zero-metadata foundation. FLAIRE achieves completely passive, metadata-free incentivization through two decoupled components: (1) a client profiling engine that infers local label distributions strictly via last-layer gradient inversion, requiring zero client-reported descriptors and zero server validation data; and (2) a label-rarity-aware incentive mechanism that assigns rewards inversely proportional to global label frequency. This design guarantees fair evaluation for rare-data providers ("Mavericks") while remaining resilient to free-riders and metadata tampering. FLAIRE is the first framework to unify passive, zero-trust profiling with rarity-aware contribution valuation in non-IID FL. Extensive evaluations on MNIST, Fashion-MNIST, EMNIST, and FEMNIST demonstrate that FLAIRE consistently boosts convergence speed and global model accuracy under extreme heterogeneity, achieving a minimum accuracy improvement of 5% across all settings and up to 10%–25% on the highly skewed benchmark scenarios tested.