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University of Bologna Cesena
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Clustering clients based on local novelty signals can revolutionize federated learning by enabling efficient and autonomous collaboration without the need for extensive computational resources.
C2FL restores robust collective adaptation in mobile, privacy-sensitive environments by leveraging spatial clustering and temporal drift mitigation strategies.
Customizable Distributed Particle Filtering can significantly enhance state estimation in heterogeneous IoT environments, balancing accuracy and communication efficiency.
Pythonistas rejoice: aggregate programming, a powerful paradigm for distributed computing, finally gets a first-class, easy-to-use implementation in your favorite language.
