5 citations · 9 across the 9 of their papers we have counts for
9 papers
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…
Phyelds: A Pythonic Framework for Aggregate Computing
Gianluca Aguzzi, Davide Domini, Nicolas Farabegoli +1
Aggregate programming is a field-based coordination paradigm with over a decade of exploration and successful applications across domains including sensor networks, robotics, and I…
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…
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…
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…
Software Engineering for Collective Cyber-Physical Ecosystems
Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito +5
Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communicat…
MacroSwarm: A Field-based Compositional Framework for Swarm Programming
Gianluca Aguzzi, Roberto Casadei, Mirko Viroli
Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achiev…