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Active switched inductor (ASL) dc–dc converters have very high step-up voltage gain but practically they suffer from high voltage stress across switches because of the resonance caused by the mismatch among the inductor values and switch parameters. This article presents a new hybrid dc-dc converter topology derived from ASL and passive switched capacitors (PSC). The proposed topology has high step-up voltage gain over wide range of duty cycle. The resonance due to mismatch of parameters is eliminated. The proposed converter has low voltage stress, low current stress on circuit elements, and common ground between input and output ports. The circuit configuration, operating principle and steady state analysis of the proposed converter are presented. The key performance parameters are compared with that of other high-gain dc–dc converters. A 300 W laboratory prototype is developed and experimental verification is carried out for the voltage step-up from 30 to 400 V. The closed-loop operation is also verified experimentally and results are presented.

Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions. Pretrained artificial intelligence (AI) models are demonstrating their increasing capability in aiding the metaverse to achieve an excellent response with negligible delay, and nowadays, many large models are collaboratively trained by various participants in a manner named collaborative deep learning (CDL). However, several security weaknesses can threaten the safety of the CDL training process, which might result in fatal attacks to either the pretrained large model or the local sensitive datasets possessed by an individual entity. In CDL, malicious participants can hide within the major innocent and silently upload deceptive parameters to degenerate the model performance, or they can abuse the downloaded parameters to construct a generative adversarial network (GAN) to acquire the private information of others illegally. To compensate for these vulnerabilities, this article proposes an adversary detection-deactivation method that can limit and isolate the access of potential malicious participants as well as quarantine and disable the GAN attack or harmful backpropagation (BP) of received threatening gradients. A detailed protection analysis has been conducted on a multiview (MV) CDL case, and results show that the protocol can effectively prevent harmful access by heuristic manner analysis and can protect the existing model by swiftly checking received gradients using only one low-cost branch with an embedded firewall.