Recent studies have demonstrated that backdoor attacks can cause a significant security threat to federated learning. Existing defense methods mainly focus on detecting or eliminating the backdoor patterns after the model is backdoored. However, these methods either cause model performance degradation or heavily rely on impractical assumptions, such as labeled clean data, which exhibit limited effectiveness in federated learning. To this end, we propose FLPurifier, a novel backdoor defense method in federated learning that can effectively purify the possible backdoor attributes before federated aggregation. Specifically, FLPurifier splits a complete model into a feature extractor and classifier, in which the extractor is trained in a decoupled contrastive manner to break the strong correlation between trigger features and the target label. Compared with existing backdoor mitigation methods, FLPurifier doesn’t rely on impractical assumptions since it can effectively purify the backdoo
Besides producing hydrogen from surplus renewables, electrolyzers can also provide grid ancillary services like enhancing the stability, resilience, and robustness of the power grid. The paper presents a novel electrical circuit model for a proton exchange membrane electrolyzer (PEMEL) that has been validated using experimental data from a 400W electrolyzer. To demonstrate its adaptive capability, the proposed 400W electrical model is scaled up to a 1 MW stack, and this system is validated by comparison to another report of 1 MW stack experimental results. Results show that the developed model reproduces very similar step responses to those reported for the 400W electrolyzer and 1 MW stack. In this paper, the developed model was then used to evaluate the grid frequency response against disturbance, possible resilience advantages from frequency control services, and frequency sensitivity analysis for a modified IEEE-13-bus-distribution-feeder system. These simulations indicate that PEME
Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.
developed countries promised to double their contributions between now and 2025. we need a road map. we need a road map for implementation also, we need to recognise this is a first stage, adaptation resources must exceed 300 million per year by 2030, half of the climate change funding must be focused on adaptation. international financial institutions and global banks need to change economic models and they need to do their share. this is to boost adaptation models and they must serve as tools to leave more financial resources to serve efforts to fight climate change. countries and communities must have access to that funding which must be funnelled