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Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning
Davide Domini, Gianluca Aguzzi, Ivana Dusparic +2
Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions.…
C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini +2
Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment. Sca…
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning
Davide Domini, Gianluca Aguzzi, Lukas Esterle +1
In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and perform…
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0
Davide Domini, Laura Erhan, Gianluca Aguzzi +4
Federated Learning offers privacy-preserving collaborative intelligence but struggles to meet the sustainability demands of emerging IoT ecosystems necessary for Society 5.0-a huma…
ProFed: a Benchmark for Proximity-based non-IID Federated Learning
Davide Domini, Gianluca Aguzzi, Mirko Viroli
In recent years, cro:flFederated learning (FL) has gained significant attention within the machine learning community. Although various FL algorithms have been proposed in the lite…
Proximity-based Self-Federated Learning
Davide Domini, Gianluca Aguzzi, Nicolas Farabegoli +2
In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their loca…