986 citations · 1.1k across the 57 of their papers we have counts for
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FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato +1
Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often li…
Routing without Forgetting
Alessio Masano, Giovanni Bellitto, Dipam Goswani +2
Continual learning in transformers is commonly addressed through parameter-efficient adaptation: prompts, adapters, or LoRA modules are specialized per task while the backbone rema…
Dream2Learn: Structured Generative Dreaming for Continual Learning
Salvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri +4
Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowl…
Pre-Forgettable Models: Prompt Learning as a Native Mechanism for Unlearning
Rutger Hendrix, Giovanni Patanè, Leonardo G. Russo +5
Foundation models have transformed multimedia analysis by enabling robust and transferable representations across diverse modalities and tasks. However, their static deployment con…
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
Luca Palazzo, Matteo Pennisi, Federica Proietto Salanitri +3
In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift.…
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +3
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GA…
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli +6
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facil…
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello +2
Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small me…
Transfer without Forgetting
Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +5
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretrainin…
Effects of Auxiliary Knowledge on Continual Learning
Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +4
In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information…
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +4
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recen…