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to collaboratively train machine learning models without sharing their data. Instead, clients exchange local model updates with a central server, which uses them to improve a global model. While this paradigm enhances
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range of computer vision tasks. Their strong representational capacity, however, comes at the price of significantly higher computational complexity and memory requirements. This poses a major challenge
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