D3em: A Dual-Layer Dynamic Debiasing Evaluation Mechanism for Client Contribution in Federated Learning
Accurate client contribution evaluation is critical for sustainable federated learning and incentive design, yet existing methods face a trade-off between trust, complexity, and robustness. We show that validation-free, similarity-based metrics can suffer from a federated noise coupling effect, where historical low-quality updates become entangled with the global trajectory, causing evaluation noise to accumulate and manifest as systematic bias across rounds. We propose D3em, a dual-layer dynamic debiasing mechanism built on a parallel local–federated dual-model training framework. D3em extracts a noise-decoupled independent value from the local branch and a collaborative value from the federated branch, and fuses them via a phase-aware weighting schedule to stabilize contribution scores throughout training. Experiments on CIFAR-10, Fashion-MNIST, and SST-5 under diverse heterogeneous partitions show that D3em improves global accuracy by 6.15 percentage points on average over representative baselines, and achieves an average fairness of 97.08% when coupled with incentive schemes. Meanwhile, D3em achieves comparable or even better end-to-end overhead in terms of total communication and total latency. The code is available at https://anonymous.4open.science/r/D_3AM-8A70