FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting
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Abstract (arXiv)
This work focuses on improving the performance and fairness of Federated Learning (FL) in non IID settings by enhancing model aggregation and boosting the training of underperforming clients. We propose FeDABoost, a novel FL framework that integrates a dynamic boosting mechanism and an adaptive gradient aggregation strategy. Inspired by the weighting mechanism of the Multiclass AdaBoost (SAMME) algorithm, our aggregation method assigns higher weights to clients with lower local error rates, thereby promoting more reliable contributions to the global model. In parallel, FeDABoost dynamically boosts underperforming clients by adjusting the focal loss focusing parameter, emphasizing hard to classify examples during local training. We have evaluated FeDABoost on three benchmark datasets MNIST, FEMNIST, and CIFAR10, and compared its performance with those of FedAvg and Ditto. The results show that FeDABoost achieves improved fairness and competitive performance.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 3 Oct 2025.
Builds on
8 of 20 references.
References · 8
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
- 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
- 2020Collaborative Fairness in Federated Learningcited by 95
- 2021Towards Fairness-Aware Federated Learningcited by 58
- 2024FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learningcited by 4
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Citing papers per year: 2026: 1.
1 paper.