1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
Attention-based Hierarchical Variational Information Bottleneck for Robust Multi-Agent Communication under Variable Bandwidth
Lukas Koch Vindbjerg, Qi Zhang, Yury Brodskiy +1
Learning-based multi-agent communication under limited bandwidth does not only require deciding what to communicate, but also structuring messages so that partial transmissions rem…
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…
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…