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Optimising Communication Overhead in Federated Learning Using NSGA-II
Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy.
A Fresh Approach to Evaluate Performance in Distributed Parallel Genetic Algorithms
This work proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) when running on a cluster of multi-core processors.
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