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Lingyu Qiu

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#federated learning Book Open access Nov 2026

FedCRA: Client Risk-aware Aggregation via Update Reconstruction for IoT-based Federated Learning

Federated Learning (FL) enables distributed model training without sharing raw data, making it particularly suitable for Internet of Things (IoT) environments. However, FL remains vulnerable to poisoning attacks, where malicious clients manipulate local updates to corrupt global model optimization. Existing defenses ma...

Jin Meng, S. Amitrano, Marzia Canzaniello et al. · ACM International Symposium... · 0 citations

Conference Jun 2026

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration...

Lingyu Qiu, Daniela Annunziata, Stefano Izzo et al. · 2026 2nd International Confe... · 0 citations