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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.…
Flexible Distributed Particle Filtering for the Internet of Things via Aggregate Computing
Angela Cortecchia, Davide Domini, Giovanni Ciatto +3
State estimation from uncertain, distributed observations is central in many cyber-physical applications. While Distributed Particle Filtering (DPF) algorithms address nonlinear an…
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…