Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...
Abstract: Federated learning is an important distributed machine learning paradigm. This study proposes a privacy-preserving data augmentation model for federated learning of heterogeneous data, which ...
The energy sector is becoming a highly connected cyber-physical ecosystem in which distributed energy resources, electric ...
A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...
The premise is straightforward — we are awash in biological data. The rapid growth of multiomics datasets (genomics, transcriptomics, proteomics, metabolomics, and radiomics) together with ...
Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that ...
Abstract: As a new paradigm that integrates clustering with federated learning, federated clustering (FC) has recently attracted increasing attention, as it addresses the practical issue of privacy ...
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