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Announce Type: new Abstract: We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless channels without sharing local data. In such systems, communication time is a primary bottleneck that constrains overall training efficiency. Unlike conventional networks that prioritize individual quality-of-service requirements, FL systems collectively aim to converge to an optimal global model as efficiently as...
Sound Effects Dataset Unification With the Universal Category System
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CRESS: Quantifying Vulnerabilities of Attack Scenarios in Hardware Reverse Engineering
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