Author

F. Giampaolo

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Jul 2026

FuGuard: Client-Level Federated Unlearning via Generative Surrogates and Optimal Transport.

FuGuard is proposed, a dual-strategy federated unlearning framework, designed for efficient and ideal client-level data removal that combines the generative surrogate, which approximates the contribution of the target client, with optimal transport regularization that softly constrains model parameter drift during unlearning.

Pian Qi, Daniela Annunziata, Chiara Jappelli et al. · IEEE Transactions on Neural... · 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 of large models on the edge.

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