Reward-Free Class-wise Fairness with Global Performance Preservation in Federated Learning

Collaborative fairness in federated learning concerns how participants’ model benefits relate to their contributions. Under label skew, a single client score can obscure substantial differences in class participation. We study participation-aware class utility allocation together with its effect on shared training. Our framework, SICAR, constructs class directions from relative client distributions on the probability simplex, without separately valuing each class and mapping those values to rewards. This reward-free direction construction is followed by a response sensitive classifier fit and an explicit rank-based amplitude rule. We then subtract each saved additive perturbation from the trained client model before aggregation. The cancellation removes the injected offset, while our analysis characterizes the remaining effect of local optimization. This allows clients to receive differentiated class-wise utility while substantially decoupling the fairness intervention from global model optimization. Experiments show that the proposed framework maintains stable contribution–utility alignment while recovering global model performance.