FedGP Resilience: A Comparative Study with Standard Federated Aggregation Methods under Adversarial Scenarios
| Tipo de publicação: | Inproceedings |
| Citação: | |
| Publication status: | Published |
| Ano: | 2026 |
| Mês: | April |
| Resumo: | Federated Learning (FL) enables decentralized model training while preserving data privacy. However, classical aggregation strategies such as FedAVG, FedPROX, and FedNOVA show significant performance degradation when client data distributions are highly non-IID. They are also vulnerable to adversarial perturbations that corrupt client updates. To overcome these limitations, FedGP has been introduced in literature, a Genetic Programming (GP)–based aggregation approach that evolves symbolic aggregation functions instead of relying on fixed rules.This study investigates the resilience of FedGP in challenging FL scenarios. Thanks to its adaptive mechanism, the server can dynamically combine, reweight, or discard client contributions according to their reliability, improving robustness against noisy or malicious updates. We conduct a comparative evaluation on PathMNIST and FashionMNIST under standard and adversarial conditions, including Gaussian noise, label-flipping, and sign-flipping attacks. All methods are tested under controlled non-IID settings with an imbalance rate of 0.8 and identical training hyperparameters to ensure fairness. Experimental results show that FedGP consistently outperforms traditional aggregation methods in terms of robustness and accuracy, particularly in the presence of corrupted clients. |
| Palavras-chave: | Aggregation method resilience, federated learning, FedGP, Genetic Programming |
| Autores | |
| Adicionado por: | [] |
| Total mark: | 0 |
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